Amino Acid
C
C++
CDF
CLT
CO2
CSV
Cartesian
Central Limit Theorem
Chebyshev
Cumulative Distribution Function
DBSCAN
DLL
DLLEXPORT
DLLIMPORT
Duluth
Elbow method
Error Function
Excel
Fibonacci sequence
Fortran
Gaussian
GitHub
GitHub pages
HTTPError
Hawaii
Honolulu
IEEE
IO
- Visualizing and comparing the temperatures of Honolulu and Duluth
- Visualizing and comparing the temperatures of Honolulu and Duluth via Excel
- Parsing data from the World Wide Web
- Data transfer: Converting formatted input to Comma-Separated-Values (CSV) output
- Command line input option-value pairs
- Data transfer: Converting Comma-Separated-Values (CSV) input to formatted output
- Reading data from the World Wide Web
- Data transfer: Parsing Amino Acid data file
- Command line input arguments summation via sum()
- Command line input arguments summation via eval()
Intel
Intel Parallel Studio
Kendall
Kmedoids
MATLAB
- A naive implementation of Kmedoids clustering
- Regression: Predicting the distribution of the a dataset subjected to a smooth censorship (sample incompleteness)
- Regression: Estimating the parameters of a linear model for a Normally-distributed sample
- Regression: Estimating the parameters of a Normally-distributed sample
- Visualizing and comparing the temperatures of Honolulu and Duluth
- Visualizing and comparing the temperatures of Honolulu and Duluth via Excel
- Computing the Spearman rank correlation coefficient of a dataset
- Visualizing the average precipitation among the US states
- Monte Carlo approximation of the number Pi using a full circle
- Monte Carlo approximation of the area of heart
- Parsing data from the World Wide Web
- Data transfer: Converting formatted input to Comma-Separated-Values (CSV) output
- Python modules and packaging
- Regression: Predicting the bivariate distribution of the a dataset subjected to censorship (sample incompleteness)
- Regression: Predicting the distribution of the a dataset subjected to censorship (sample incompleteness)
- Best visualization coloring
- Regression: Predicting the global land temperature of the Earth in 2050 from the past data via the maximum likelihood approach
- Regression: Predicting the global land temperature of the Earth in 2050 from the past data: linear vs. exponential temperature increase
- Regression: obtaining the most likely mean of a set of Standard Normally Distributed Random Variables
- Regression: obtaining the most likely mean of a set of Standard Normally Distributed Random Variables via the least absolute deviations method
- Regression: obtaining the most likely mean of a set of Standard Normally Distributed Random Variables via the least absolute deviations method
- Regression: Predicting the global land temperature of the Earth in 2050 from the past data
- Understanding the Central Limit Theorem via random walk
- Data transfer: Converting Comma-Separated-Values (CSV) input to formatted output
- The while-loop implementation of for-loop
- value, variable, type, syntax error
- Computing the area of a triangle
- Time required for cooking a refrigerated egg
- String concatenation using for-loop
- String concatenation using for-loop I
- Simulating the Monty Hall game
- Impact of round-off errors on numerical computations
- Regression: obtaining the most likely mean and standard deviation of a set of Standard Normally Distributed Random Variables
- Reading data from the World Wide Web
- Impact of machine precision on numerical computation
- Converting polar and Cartesian vector representations using functions and structures
- Operator precedence
- Getting the boundary of objects in images
- Monte Carlo approximation of the number Pi
- Matrix Initialization
- MATLAB working directory
- Subplots in MATLAB
- MATLAB script full of errors
- Getting the largest prime number smaller than the input value
- Checking if an input is a prime number (via recursive function calls)?
- Integer overflow
- Implementing the Bell-shaped (Gaussian) function
- Function generators
- Computing the Fibonacci sequence via recursive function calls
- Computing the Fibonacci sequence via for-loop
- Calculating the size of a directory
MCMC
MKL
MVN
Markov Chain
Microsoft Visual Studio
Minnesota
Monte Carlo
- Regression: Predicting the distribution of the a dataset subjected to a smooth censorship (sample incompleteness)
- Regression: Estimating the parameters of a linear model for a Normally-distributed sample
- Regression: Estimating the parameters of a Normally-distributed sample
- Computing and removing the autocorrelation of a dataset
- Monte Carlo approximation of the number Pi
- Monte Carlo approximation of the number Pi using a full circle
- Monte Carlo approximation of the area of heart
- Regression: Predicting the bivariate distribution of the a dataset subjected to censorship (sample incompleteness)
- Regression: Predicting the distribution of the a dataset subjected to censorship (sample incompleteness)
- Regression: Predicting the global land temperature of the Earth in 2050 from the past data via the maximum likelihood approach
- Regression: obtaining the most likely mean of a set of Standard Normally Distributed Random Variables
- Regression: obtaining the most likely mean of a set of Standard Normally Distributed Random Variables via the least absolute deviations method
- Regression: obtaining the most likely mean of a set of Standard Normally Distributed Random Variables via the least absolute deviations method
- Monte Carlo sampling of the sum of two Gaussian distributions
- Understanding the Central Limit Theorem via random walk
- Simulating the Monty Hall game
- Regression: obtaining the most likely mean and standard deviation of a set of Standard Normally Distributed Random Variables
- Monte Carlo sampling of distribution functions
- Monte Carlo approximation of the number Pi
Normal distribution
OOP
PDF
- Regression: Predicting the distribution of the a dataset subjected to a smooth censorship (sample incompleteness)
- Regression: Predicting the global land temperature of Earth in 2050 from the past data: Choosing the best model
- Regression: Estimating the parameters of a linear model for a Normally-distributed sample
- Regression: Estimating the parameters of a Normally-distributed sample
- Regression: Predicting the bivariate distribution of the a dataset subjected to censorship (sample incompleteness)
- Regression: Predicting the distribution of the a dataset subjected to censorship (sample incompleteness)
- Best visualization coloring
- Regression: Predicting the global land temperature of the Earth in 2050 from the past data via the maximum likelihood approach
- Regression: Predicting the global land temperature of the Earth in 2050 from the past data: linear vs. exponential temperature increase
- Regression: obtaining the most likely mean of a set of Standard Normally Distributed Random Variables
- Regression: obtaining the most likely mean of a set of Standard Normally Distributed Random Variables via the least absolute deviations method
- Regression: obtaining the most likely mean of a set of Standard Normally Distributed Random Variables via the least absolute deviations method
- Monte Carlo sampling of the sum of two Gaussian distributions
- Regression: Predicting the global land temperature of the Earth in 2050 from the past data
- Regression: obtaining the most likely mean and standard deviation of a set of Standard Normally Distributed Random Variables
- Monte Carlo sampling of distribution functions
ParaDRAM
ParaMonte
Pearson
Python
- Performance benchmarking of naive matrix multiplication in Python vs. optimized libraries
- Performance benchmarking of naive matrix multiplication: naive vs. matmul vs. MKL
- Performance optimization: loop invariant code motion
- Performance optimization: Searching sorted array via linear vs. binary search
- Performance optimization: Forced reduction - sin(x)cos(y) + cos(x)sin(y)
- Performance optimization: Forced reduction - sincos
- Performance optimization: Forced reduction - hidden opportunities
- Performance optimization: Breaking out of loop early
- A naive implementation of Kmedoids clustering
- Kmeans clustering - an implementation
- Kmeans clustering: Determining the cluster number using the Elbow method
- Regression: Predicting the distribution of the a dataset subjected to a smooth censorship (sample incompleteness)
- Regression: Estimating the parameters of a linear model for a Normally-distributed sample
- Regression: Estimating the parameters of a Normally-distributed sample
- Computing the cross-correlation of sin() and cos()
- Computing the cross-correlation of two data attributes
- Computing the autocorrelation of a dataset
- Computing and removing the autocorrelation of a dataset
- Visualizing and comparing the temperatures of Honolulu and Duluth
- Visualizing and comparing the temperatures of Honolulu and Duluth via Excel
- Computing the covariance matrix of a dataset
- Computing the covariance matrix from the correlation matrix and standard deviations
- Computing the correlation matrix of a dataset
- Prove that the diagonal elements of a correlation matrix of a dataset must be one
- Computing the Spearman rank correlation coefficient of a dataset
- Computing the Pearson correlation coefficient of a dataset
- The most sensitive correlation coefficient to outliers
- Computing the Kendall's rank correlation coefficient of a dataset
- Visualizing the average precipitation among the US states
- Computing the first four moments of a sample
- An experimental proof of Chebyshev's inequality
- Computing the mean of a weighted data
- Monte Carlo approximation of the number Pi
- Monte Carlo approximation of the number Pi using a full circle
- Monte Carlo approximation of the area of heart
- Parsing data from the World Wide Web
- Data transfer: Converting formatted input to Comma-Separated-Values (CSV) output
- Command line input option-value pairs
- Python modules and packaging
- Regression: Predicting the bivariate distribution of the a dataset subjected to censorship (sample incompleteness)
- Regression: Predicting the distribution of the a dataset subjected to censorship (sample incompleteness)
- Regression: Predicting the global land temperature of the Earth in 2050 from the past data via the maximum likelihood approach
- Regression: Predicting the global land temperature of the Earth in 2050 from the past data: linear vs. exponential temperature increase
- Projectile motion implementation through OOP multiple inheritance
- Parabola as a subclass of line
- Implementing an integration problem via an integrand object
- Regression: obtaining the most likely mean of a set of Standard Normally Distributed Random Variables
- Regression: obtaining the most likely mean of a set of Standard Normally Distributed Random Variables via the least absolute deviations method
- Regression: obtaining the most likely mean of a set of Standard Normally Distributed Random Variables via the least absolute deviations method
- Monte Carlo sampling of the sum of two Gaussian distributions
- Kmeans clustering
- Regression: Predicting the global land temperature of the Earth in 2050 from the past data
- Understanding the Central Limit Theorem via random walk
- Finding the maximum value of an array via recursive function calls
- Finding the position of the maximum value of an array via recursive function calls
- Calling Fortran function from other languages via a Dynamic-Link Library (DLL): getPower()
- Data transfer: Converting Comma-Separated-Values (CSV) input to formatted output
- The while-loop implementation of for-loop
- value, variable, type, syntax error
- Computing the area of a triangle
- Time required for cooking a refrigerated egg
- String concatenation using for-loop
- Simulating the Monty Hall game
- Impact of round-off errors on numerical computations
- Reading data from the World Wide Web
- Python aliasing vs. copying variables
- Single-line Python input and string manipulation
- Python script full of syntax errors
- Python script full of errors
- Python dictionary of class members
- Python script call from the Bash command line
- Impact of machine precision on numerical computation
- Operator precedence
- Check if number is even in one line function definition
- Monte Carlo sampling of distribution functions
- Monte Carlo approximation of the number Pi
- Modifying the index of a for-loop
- Matrix Initialization
- Getting the largest prime number smaller than the input value
- Checking if an input is a prime number (via recursive function calls)?
- Integer overflow
- Implementing the Bell-shaped (Gaussian) function
- Computing the Fibonacci sequence via recursive function calls
- Computing the Fibonacci sequence via for-loop
- Exception handling in the case of a simple projectile motion
- Exception handling in the case of division by zero
- Data transfer: Parsing Amino Acid data file
- Command line input arguments summation via sum()
- Command line input arguments summation via eval()
- Branching, the Pythonic way
Simpson
Spearman
VCS
ValueError
Windows
alias
area
argument
array
assert
autocorrelation
bayesian
bell-shaped
bias
binary
bind
binning
bivariate
boolean
- Logic NAND and NOR
- Logical implication in terms of logic functions
- Logic NAND equivalence
- The fundamental desiderata of Probability Theory
- Probability Theory: correspondence with commonsense
- The proof of Bayes' Rule via Venn diagram
- The fundamental logical operators
- Logic functions in terms of logic functions
- Logic functions with 2 input
- Logic functions with 1 input
- The two types of scientific reasoning
- Logical product denial
- Policeman, jewelry, and burglar
- Logical implication
- Logic implication, denial, equivalence
- Venn diagram representation of Boolean identities
- Truth table representation of Boolean identities
branching
- Python modules and packaging
- Finding the maximum value of an array via recursive function calls
- Finding the position of the maximum value of an array via recursive function calls
- Single-line Python input and string manipulation
- Converting polar and Cartesian vector representations using functions and structures
- Getting the largest prime number smaller than the input value
- Checking if an input is a prime number (via recursive function calls)?
- Function generators
- Computing the Fibonacci sequence via recursive function calls
- Computing the Fibonacci sequence via for-loop
- Branching, the Pythonic way
bwboundaries
carbon
cd
cell array
censored
char
choropleth
class
climate
clustering
cognitive
colorscale
command line
command-line
commonsense
compiler
concatenation
coordinates
copy
correlation
- Computing the cross-correlation of sin() and cos()
- Computing the cross-correlation of two data attributes
- Computing the autocorrelation of a dataset
- Computing and removing the autocorrelation of a dataset
- Computing the covariance matrix of a dataset
- Computing the covariance matrix from the correlation matrix and standard deviations
- Computing the correlation matrix of a dataset
- Computing the correlation matrix of a dataset
- Prove that the diagonal elements of a correlation matrix of a dataset must be one
- Prove that the diagonal elements of a correlation matrix of a dataset must be one
- Computing the Spearman rank correlation coefficient of a dataset
- Computing the Pearson correlation coefficient of a dataset
- The most sensitive correlation coefficient to outliers
- Computing the Kendall's rank correlation coefficient of a dataset
cos
covariance
crosscorrelation
ctypes
data
data transfer
deduction
- Logic NAND and NOR
- Logical implication in terms of logic functions
- Logic NAND equivalence
- The fundamental desiderata of Probability Theory
- Probability Theory: correspondence with commonsense
- The proof of Bayes' Rule via Venn diagram
- The fundamental logical operators
- Logic functions in terms of logic functions
- Logic functions with 2 input
- Logic functions with 1 input
- The two types of scientific reasoning
- Logical product denial
- Policeman, jewelry, and burglar
- Logical implication
- Logic implication, denial, equivalence
- Venn diagram representation of Boolean identities
- Truth table representation of Boolean identities
density
derivative
diag
diagram
dictionary
differentiation
digonal
dir
directory
disp
distribution
distribution function
- Regression: Predicting the distribution of the a dataset subjected to a smooth censorship (sample incompleteness)
- Regression: Predicting the global land temperature of Earth in 2050 from the past data: Choosing the best model
- Regression: Estimating the parameters of a linear model for a Normally-distributed sample
- Regression: Estimating the parameters of a Normally-distributed sample
- Regression: Predicting the bivariate distribution of the a dataset subjected to censorship (sample incompleteness)
- Regression: Predicting the distribution of the a dataset subjected to censorship (sample incompleteness)
- Best visualization coloring
- Regression: Predicting the global land temperature of the Earth in 2050 from the past data via the maximum likelihood approach
- Regression: Predicting the global land temperature of the Earth in 2050 from the past data: linear vs. exponential temperature increase
- Regression: obtaining the most likely mean of a set of Standard Normally Distributed Random Variables
- Regression: obtaining the most likely mean of a set of Standard Normally Distributed Random Variables via the least absolute deviations method
- Regression: obtaining the most likely mean of a set of Standard Normally Distributed Random Variables via the least absolute deviations method
- Monte Carlo sampling of the sum of two Gaussian distributions
- Regression: Predicting the global land temperature of the Earth in 2050 from the past data
- Regression: obtaining the most likely mean and standard deviation of a set of Standard Normally Distributed Random Variables
- Monte Carlo sampling of distribution functions
dynamic-link library
egg
equation
error
escape character
eval
even
exception
exception handling
exponential
eye
fieldnames
- Better visualizations through axes transformations
- Kmeans clustering - an implementation
- Kmeans clustering: Determining the cluster number using the Elbow method
- Regression: Predicting the distribution of the a dataset subjected to a smooth censorship (sample incompleteness)
- Regression: Predicting the global land temperature of Earth in 2050 from the past data: Choosing the best model
- Regression: Estimating the parameters of a linear model for a Normally-distributed sample
- Regression: Estimating the parameters of a Normally-distributed sample
- Regression: Model selection for a bivariate data using Excel
- Visualizing and comparing the temperatures of Honolulu and Duluth
- Visualizing and comparing the temperatures of Honolulu and Duluth via Excel
- Visualizing the average precipitation of the US states vs. sunshine
- Visualizing the average precipitation among the US states
- Monte Carlo approximation of the number Pi
- Monte Carlo approximation of the number Pi using a full circle
- Monte Carlo approximation of the area of heart
- Parsing data from the World Wide Web
- Regression: Predicting the bivariate distribution of the a dataset subjected to censorship (sample incompleteness)
- Regression: Predicting the distribution of the a dataset subjected to censorship (sample incompleteness)
- Best visualization coloring
- Regression: Predicting the global land temperature of the Earth in 2050 from the past data via the maximum likelihood approach
- Regression: Predicting the global land temperature of the Earth in 2050 from the past data: linear vs. exponential temperature increase
- Regression: obtaining the most likely mean of a set of Standard Normally Distributed Random Variables
- Regression: obtaining the most likely mean of a set of Standard Normally Distributed Random Variables via the least absolute deviations method
- Regression: obtaining the most likely mean of a set of Standard Normally Distributed Random Variables via the least absolute deviations method
- Monte Carlo sampling of the sum of two Gaussian distributions
- Kmeans clustering
- Regression: Predicting the global land temperature of the Earth in 2050 from the past data
- Simulating the Monty Hall game
- Regression: obtaining the most likely mean and standard deviation of a set of Standard Normally Distributed Random Variables
- Reading data from the World Wide Web
- Getting the boundary of objects in images
- Monte Carlo sampling of distribution functions
- Monte Carlo approximation of the number Pi
- MATLAB working directory
- Subplots in MATLAB
float
for-loop
- Data transfer: Converting formatted input to Comma-Separated-Values (CSV) output
- Data transfer: Converting Comma-Separated-Values (CSV) input to formatted output
- The while-loop implementation of for-loop
- String concatenation using for-loop
- String concatenation using for-loop I
- Impact of round-off errors on numerical computations
- Impact of machine precision on numerical computation
- Modifying the index of a for-loop
- Getting the largest prime number smaller than the input value
- Computing the Fibonacci sequence via for-loop
- Data transfer: Parsing Amino Acid data file
free-fall
frequentist
function
- Data transfer: Converting formatted input to Comma-Separated-Values (CSV) output
- Python modules and packaging
- Making and using multiple Dynamic-Link Libraries (DLL) from within a single executable using the Intel Fortran Compiler on Windows OS
- Finding the maximum value of an array via recursive function calls
- Finding the position of the maximum value of an array via recursive function calls
- Making and using a Dynamic-Link Library (DLL) from a Fortran procedure using Intel Fortran Compiler on Windows OS: getSquare()
- Calling Fortran function from other languages via a Dynamic-Link Library (DLL): getPower()
- Passing characters and strings from C to Fortran and from Fortran to C
- Passing allocatable string from Fortran to C
- Data transfer: Converting Comma-Separated-Values (CSV) input to formatted output
- Computing the area of a triangle
- Converting polar and Cartesian vector representations using functions and structures
- Check if number is even in one line function definition
- Getting the largest prime number smaller than the input value
- Checking if an input is a prime number (via recursive function calls)?
- Function generators
- Computing the Fibonacci sequence via recursive function calls
- Computing the Fibonacci sequence via for-loop
- Exception handling in the case of division by zero
function generator
function handle
generator
geography
git
git branch
global
gravity
heat capacity
histogram
hpc
id
image
imagesc
implication
- Logic NAND and NOR
- Logical implication in terms of logic functions
- Logic NAND equivalence
- The fundamental desiderata of Probability Theory
- Probability Theory: correspondence with commonsense
- The proof of Bayes' Rule via Venn diagram
- The fundamental logical operators
- Logic functions in terms of logic functions
- Logic functions with 2 input
- Logic functions with 1 input
- Logical implication
- Logic implication, denial, equivalence
inequality
inheritance
initialization
- Better visualizations through axes transformations
- Visualizing the average precipitation of the US states vs. sunshine
- Visualizing the average precipitation among the US states
- Parsing data from the World Wide Web
- Data transfer: Converting formatted input to Comma-Separated-Values (CSV) output
- Command line input option-value pairs
- Python modules and packaging
- Data transfer: Converting Comma-Separated-Values (CSV) input to formatted output
- Reading data from the World Wide Web
- Single-line Python input and string manipulation
- Computing the Fibonacci sequence via recursive function calls
- Computing the Fibonacci sequence via for-loop
- Data transfer: Parsing Amino Acid data file
- Command line input arguments summation via sum()
- Command line input arguments summation via eval()
- Branching, the Pythonic way
installation
instantiation
int32
integer
integration
interoperation
io
is
isfield
iso_c_binding
iso_fortran_env
isprime
isreal
kernel
kmeans
kmeans++
kurtosis
least squares method
library
line
- Regression: Predicting the distribution of the a dataset subjected to a smooth censorship (sample incompleteness)
- Regression: Predicting the global land temperature of Earth in 2050 from the past data: Choosing the best model
- Regression: Estimating the parameters of a linear model for a Normally-distributed sample
- Regression: Estimating the parameters of a Normally-distributed sample
- Regression: Model selection for a bivariate data using Excel
- Visualizing and comparing the temperatures of Honolulu and Duluth
- Visualizing and comparing the temperatures of Honolulu and Duluth via Excel
- Monte Carlo approximation of the number Pi
- Monte Carlo approximation of the number Pi using a full circle
- Monte Carlo approximation of the area of heart
- Regression: Predicting the bivariate distribution of the a dataset subjected to censorship (sample incompleteness)
- Regression: Predicting the distribution of the a dataset subjected to censorship (sample incompleteness)
- Best visualization coloring
- Regression: Predicting the global land temperature of the Earth in 2050 from the past data via the maximum likelihood approach
- Regression: Predicting the global land temperature of the Earth in 2050 from the past data: linear vs. exponential temperature increase
- Regression: obtaining the most likely mean of a set of Standard Normally Distributed Random Variables
- Regression: obtaining the most likely mean of a set of Standard Normally Distributed Random Variables via the least absolute deviations method
- Regression: obtaining the most likely mean of a set of Standard Normally Distributed Random Variables via the least absolute deviations method
- Monte Carlo sampling of the sum of two Gaussian distributions
- Regression: Predicting the global land temperature of the Earth in 2050 from the past data
- Simulating the Monty Hall game
- Regression: obtaining the most likely mean and standard deviation of a set of Standard Normally Distributed Random Variables
- Monte Carlo sampling of distribution functions
- Monte Carlo approximation of the number Pi
linear
list
- Finding the maximum value of an array via recursive function calls
- Finding the position of the maximum value of an array via recursive function calls
- value, variable, type, syntax error
- Computing the area of a triangle
- String concatenation using for-loop
- String concatenation using for-loop I
- Python aliasing vs. copying variables
logarithm
logarithmic
logic
- Logic NAND and NOR
- Logical implication in terms of logic functions
- Logic NAND equivalence
- The fundamental desiderata of Probability Theory
- Probability Theory: correspondence with commonsense
- The proof of Bayes' Rule via Venn diagram
- The fundamental logical operators
- Logic functions in terms of logic functions
- Logic functions with 2 input
- Logic functions with 1 input
- The two types of scientific reasoning
- Logical product denial
- Policeman, jewelry, and burglar
- Logical implication
- Logic implication, denial, equivalence
- Venn diagram representation of Boolean identities
- Truth table representation of Boolean identities
logical
loop
map
markdown
matchstick
matmul
matplotlib
- A naive implementation of Kmedoids clustering
- Kmeans clustering - an implementation
- Kmeans clustering: Determining the cluster number using the Elbow method
- Visualizing the average precipitation among the US states
- Monte Carlo approximation of the number Pi
- Monte Carlo approximation of the number Pi using a full circle
- Monte Carlo approximation of the area of heart
- Parsing data from the World Wide Web
- Monte Carlo sampling of the sum of two Gaussian distributions
- Kmeans clustering
- Simulating the Monty Hall game
- Reading data from the World Wide Web
- Monte Carlo sampling of distribution functions
- Monte Carlo approximation of the number Pi
matrix
maximum
maximum likelihood method
mean
memory
midpoint
mkdir
modulo
modulus
moments
moving average
multiple assignment
multiplication
name mangling
nand
nargin
nested function
nested list
nor
normal
num2str
numpy
- Kmeans clustering - an implementation
- Kmeans clustering: Determining the cluster number using the Elbow method
- Monte Carlo approximation of the number Pi
- Monte Carlo approximation of the number Pi using a full circle
- Monte Carlo approximation of the area of heart
- Monte Carlo sampling of the sum of two Gaussian distributions
- Kmeans clustering
- Monte Carlo sampling of distribution functions
- Monte Carlo approximation of the number Pi
- Matrix Initialization
object
object boundary
objective function
- Regression: Predicting the distribution of the a dataset subjected to a smooth censorship (sample incompleteness)
- Regression: Predicting the global land temperature of Earth in 2050 from the past data: Choosing the best model
- Regression: Estimating the parameters of a linear model for a Normally-distributed sample
- Regression: Estimating the parameters of a Normally-distributed sample
- Regression: Predicting the bivariate distribution of the a dataset subjected to censorship (sample incompleteness)
- Regression: Predicting the distribution of the a dataset subjected to censorship (sample incompleteness)
- Best visualization coloring
- Regression: Predicting the global land temperature of the Earth in 2050 from the past data via the maximum likelihood approach
- Regression: Predicting the global land temperature of the Earth in 2050 from the past data: linear vs. exponential temperature increase
- Regression: obtaining the most likely mean of a set of Standard Normally Distributed Random Variables
- Regression: obtaining the most likely mean of a set of Standard Normally Distributed Random Variables via the least absolute deviations method
- Regression: obtaining the most likely mean of a set of Standard Normally Distributed Random Variables via the least absolute deviations method
- Regression: Predicting the global land temperature of the Earth in 2050 from the past data
- Regression: obtaining the most likely mean and standard deviation of a set of Standard Normally Distributed Random Variables
operator
operator precedence
optimization
output
overflow
pandas
parabola
- Performance benchmarking of naive matrix multiplication in Python vs. optimized libraries
- Performance benchmarking of naive matrix multiplication: naive vs. matmul vs. MKL
- Performance optimization: loop invariant code motion
- Performance optimization: Searching sorted array via linear vs. binary search
- Performance optimization: Forced reduction - sin(x)cos(y) + cos(x)sin(y)
- Performance optimization: Forced reduction - sincos
- Performance optimization: Forced reduction - hidden opportunities
- Performance optimization: Breaking out of loop early
- The while-loop implementation of for-loop
- Computing the Fibonacci sequence via for-loop
periodic
physics
pi
plausibility
- Logic NAND and NOR
- Logical implication in terms of logic functions
- Logic NAND equivalence
- The major schools of thought in Probability Theory
- The fundamental desiderata of Probability Theory
- Probability Theory: correspondence with commonsense
- The proof of Bayes' Rule via Venn diagram
- The fundamental logical operators
- Logic functions in terms of logic functions
- Logic functions with 2 input
- Logic functions with 1 input
- The two types of scientific reasoning
- Logical product denial
- Policeman, jewelry, and burglar
- Logical implication
- Logic implication, denial, equivalence
- Venn diagram representation of Boolean identities
- Truth table representation of Boolean identities
plot
- A naive implementation of Kmedoids clustering
- Online comparison of the Kmeans clustering algorithm with DBSCAN
- Online experimentation with DBSCAN clustering technique
- Kmeans clustering - an implementation
- Kmeans clustering: Determining the cluster number using the Elbow method
- Regression: Predicting the distribution of the a dataset subjected to a smooth censorship (sample incompleteness)
- Regression: Predicting the global land temperature of Earth in 2050 from the past data: Choosing the best model
- Regression: Estimating the parameters of a linear model for a Normally-distributed sample
- Regression: Estimating the parameters of a Normally-distributed sample
- Ugly visualization
- The population growths of the US states
- The cities with the most and least moderate temperature
- Wrong visualization
- Excel Bar plot
- Visualization color scales
- Regression: Model selection for a bivariate data using Excel
- Visualizing and comparing the temperatures of Honolulu and Duluth
- Visualizing and comparing the temperatures of Honolulu and Duluth via Excel
- Monte Carlo approximation of the number Pi
- Monte Carlo approximation of the number Pi using a full circle
- Monte Carlo approximation of the area of heart
- Parsing data from the World Wide Web
- Visualization: The world population
- Visualization: The world population (refined)
- Visualization: The world map resized by population
- Visualization: Worldwide Internet Usage
- Regression: Predicting the bivariate distribution of the a dataset subjected to censorship (sample incompleteness)
- Regression: Predicting the distribution of the a dataset subjected to censorship (sample incompleteness)
- Best visualization coloring
- Regression: Predicting the global land temperature of the Earth in 2050 from the past data via the maximum likelihood approach
- Regression: Predicting the global land temperature of the Earth in 2050 from the past data: linear vs. exponential temperature increase
- Regression: obtaining the most likely mean of a set of Standard Normally Distributed Random Variables
- Regression: obtaining the most likely mean of a set of Standard Normally Distributed Random Variables via the least absolute deviations method
- Regression: obtaining the most likely mean of a set of Standard Normally Distributed Random Variables via the least absolute deviations method
- Monte Carlo sampling of the sum of two Gaussian distributions
- Kmeans clustering
- Regression: Predicting the global land temperature of the Earth in 2050 from the past data
- Simulating the Monty Hall game
- Regression: obtaining the most likely mean and standard deviation of a set of Standard Normally Distributed Random Variables
- Reading data from the World Wide Web
- Getting the boundary of objects in images
- Monte Carlo sampling of distribution functions
- Monte Carlo approximation of the number Pi
- MATLAB working directory
- Subplots in MATLAB
polar
polynomial
population
precedence
precision
prime number
print
probability
- Regression: Predicting the distribution of the a dataset subjected to a smooth censorship (sample incompleteness)
- Regression: Predicting the global land temperature of Earth in 2050 from the past data: Choosing the best model
- Regression: Estimating the parameters of a linear model for a Normally-distributed sample
- Regression: Estimating the parameters of a Normally-distributed sample
- Monte Carlo approximation of the number Pi
- Monte Carlo approximation of the number Pi using a full circle
- Monte Carlo approximation of the area of heart
- Regression: Predicting the bivariate distribution of the a dataset subjected to censorship (sample incompleteness)
- Regression: Predicting the distribution of the a dataset subjected to censorship (sample incompleteness)
- Best visualization coloring
- Regression: Predicting the global land temperature of the Earth in 2050 from the past data via the maximum likelihood approach
- Regression: Predicting the global land temperature of the Earth in 2050 from the past data: linear vs. exponential temperature increase
- Regression: obtaining the most likely mean of a set of Standard Normally Distributed Random Variables
- Regression: obtaining the most likely mean of a set of Standard Normally Distributed Random Variables via the least absolute deviations method
- Regression: obtaining the most likely mean of a set of Standard Normally Distributed Random Variables via the least absolute deviations method
- Monte Carlo sampling of the sum of two Gaussian distributions
- Regression: Predicting the global land temperature of the Earth in 2050 from the past data
- Understanding the Central Limit Theorem via random walk
- Simulating the Monty Hall game
- Regression: obtaining the most likely mean and standard deviation of a set of Standard Normally Distributed Random Variables
- Monte Carlo sampling of distribution functions
- Monte Carlo approximation of the number Pi
probability density function
- Regression: Predicting the distribution of the a dataset subjected to a smooth censorship (sample incompleteness)
- Regression: Predicting the global land temperature of Earth in 2050 from the past data: Choosing the best model
- Regression: Estimating the parameters of a linear model for a Normally-distributed sample
- Regression: Estimating the parameters of a Normally-distributed sample
- Regression: Predicting the bivariate distribution of the a dataset subjected to censorship (sample incompleteness)
- Regression: Predicting the distribution of the a dataset subjected to censorship (sample incompleteness)
- Best visualization coloring
- Regression: Predicting the global land temperature of the Earth in 2050 from the past data via the maximum likelihood approach
- Regression: Predicting the global land temperature of the Earth in 2050 from the past data: linear vs. exponential temperature increase
- Regression: obtaining the most likely mean of a set of Standard Normally Distributed Random Variables
- Regression: obtaining the most likely mean of a set of Standard Normally Distributed Random Variables via the least absolute deviations method
- Regression: obtaining the most likely mean of a set of Standard Normally Distributed Random Variables via the least absolute deviations method
- Monte Carlo sampling of the sum of two Gaussian distributions
- Regression: Predicting the global land temperature of the Earth in 2050 from the past data
- Regression: obtaining the most likely mean and standard deviation of a set of Standard Normally Distributed Random Variables
- Monte Carlo sampling of distribution functions
project
projectile
projectile motion
puzzle
quadratic
raise
random
random number
- Kmeans clustering - an implementation
- Kmeans clustering: Determining the cluster number using the Elbow method
- Regression: Predicting the distribution of the a dataset subjected to a smooth censorship (sample incompleteness)
- Regression: Predicting the global land temperature of Earth in 2050 from the past data: Choosing the best model
- Regression: Estimating the parameters of a linear model for a Normally-distributed sample
- Regression: Estimating the parameters of a Normally-distributed sample
- Regression: Model selection for a bivariate data using Excel
- Monte Carlo approximation of the number Pi
- Monte Carlo approximation of the number Pi using a full circle
- Monte Carlo approximation of the area of heart
- Regression: Predicting the bivariate distribution of the a dataset subjected to censorship (sample incompleteness)
- Regression: Predicting the distribution of the a dataset subjected to censorship (sample incompleteness)
- Best visualization coloring
- Regression: Predicting the global land temperature of the Earth in 2050 from the past data via the maximum likelihood approach
- Regression: Predicting the global land temperature of the Earth in 2050 from the past data: linear vs. exponential temperature increase
- Regression: obtaining the most likely mean of a set of Standard Normally Distributed Random Variables
- Regression: obtaining the most likely mean of a set of Standard Normally Distributed Random Variables via the least absolute deviations method
- Regression: obtaining the most likely mean of a set of Standard Normally Distributed Random Variables via the least absolute deviations method
- Monte Carlo sampling of the sum of two Gaussian distributions
- Kmeans clustering
- Regression: Predicting the global land temperature of the Earth in 2050 from the past data
- Simulating the Monty Hall game
- Regression: obtaining the most likely mean and standard deviation of a set of Standard Normally Distributed Random Variables
- Monte Carlo sampling of distribution functions
- Monte Carlo approximation of the number Pi
random walk
read_csv
real
real128
real32
real64
reasoning
- Logic NAND and NOR
- Logical implication in terms of logic functions
- Logic NAND equivalence
- The fundamental desiderata of Probability Theory
- Probability Theory: correspondence with commonsense
- The proof of Bayes' Rule via Venn diagram
- The fundamental logical operators
- Logic functions in terms of logic functions
- Logic functions with 2 input
- Logic functions with 1 input
- The two types of scientific reasoning
- Logical product denial
- Policeman, jewelry, and burglar
- Logical implication
- Logic implication, denial, equivalence
- Venn diagram representation of Boolean identities
- Truth table representation of Boolean identities
recursive
recursive function
regression
- Regression: Predicting the distribution of the a dataset subjected to a smooth censorship (sample incompleteness)
- Regression: Predicting the global land temperature of Earth in 2050 from the past data: Choosing the best model
- Regression: Estimating the parameters of a linear model for a Normally-distributed sample
- Regression: Estimating the parameters of a Normally-distributed sample
- Regression: Model selection for a bivariate data using Excel
- Regression: Predicting the bivariate distribution of the a dataset subjected to censorship (sample incompleteness)
- Regression: Predicting the distribution of the a dataset subjected to censorship (sample incompleteness)
- Best visualization coloring
- Regression: Predicting the global land temperature of the Earth in 2050 from the past data via the maximum likelihood approach
- Regression: Predicting the global land temperature of the Earth in 2050 from the past data: linear vs. exponential temperature increase
- Regression: obtaining the most likely mean of a set of Standard Normally Distributed Random Variables
- Regression: obtaining the most likely mean of a set of Standard Normally Distributed Random Variables via the least absolute deviations method
- Regression: obtaining the most likely mean of a set of Standard Normally Distributed Random Variables via the least absolute deviations method
- Regression: Predicting the global land temperature of the Earth in 2050 from the past data
- Regression: obtaining the most likely mean and standard deviation of a set of Standard Normally Distributed Random Variables
rejection sampling
round
roundoff
run
sample
- Computing the cross-correlation of sin() and cos()
- Computing the cross-correlation of two data attributes
- Computing the autocorrelation of a dataset
- Computing and removing the autocorrelation of a dataset
- Computing the covariance matrix of a dataset
- Computing the covariance matrix from the correlation matrix and standard deviations
- Computing the correlation matrix of a dataset
- Prove that the diagonal elements of a correlation matrix of a dataset must be one
- Computing the Spearman rank correlation coefficient of a dataset
- Computing the Pearson correlation coefficient of a dataset
- The most sensitive correlation coefficient to outliers
- Computing the Kendall's rank correlation coefficient of a dataset
- Computing the first four moments of a sample
- An experimental proof of Chebyshev's inequality
- Computing the mean of a weighted data
sample incompleteness
scatter plot
schools
scikit-learn
script
search
semantic error
simulation
- Monte Carlo approximation of the number Pi
- Monte Carlo approximation of the number Pi using a full circle
- Monte Carlo approximation of the area of heart
- Regression: obtaining the most likely mean of a set of Standard Normally Distributed Random Variables
- Regression: obtaining the most likely mean of a set of Standard Normally Distributed Random Variables via the least absolute deviations method
- Regression: obtaining the most likely mean of a set of Standard Normally Distributed Random Variables via the least absolute deviations method
- Monte Carlo sampling of the sum of two Gaussian distributions
- Simulating the Monty Hall game
- Regression: obtaining the most likely mean and standard deviation of a set of Standard Normally Distributed Random Variables
- Monte Carlo sampling of distribution functions
- Monte Carlo approximation of the number Pi
sin
size
skewness
sorting
sqrt
statistics
- Computing the cross-correlation of sin() and cos()
- Computing the cross-correlation of two data attributes
- Computing the autocorrelation of a dataset
- Computing and removing the autocorrelation of a dataset
- Computing the covariance matrix of a dataset
- Computing the covariance matrix from the correlation matrix and standard deviations
- Computing the correlation matrix of a dataset
- Prove that the diagonal elements of a correlation matrix of a dataset must be one
- Computing the Spearman rank correlation coefficient of a dataset
- Computing the Pearson correlation coefficient of a dataset
- The most sensitive correlation coefficient to outliers
- Computing the Kendall's rank correlation coefficient of a dataset
- Computing the first four moments of a sample
- An experimental proof of Chebyshev's inequality
- Computing the mean of a weighted data
std
storage
str2double
string
- Parsing data from the World Wide Web
- Python modules and packaging
- Passing characters and strings from C to Fortran and from Fortran to C
- Passing allocatable string from Fortran to C
- String concatenation using for-loop
- String concatenation using for-loop I
- Reading data from the World Wide Web
- Single-line Python input and string manipulation
- Python script full of errors
- Python script call from the Bash command line
- MATLAB script full of errors
- Computing the Fibonacci sequence via recursive function calls
- Computing the Fibonacci sequence via for-loop
structure
subplot
sum
switch
syntax
syntax error
tab
table
temperature
texas
timeit
timing
triangle
truth
try-catch
try-except
tuple
type
- value, variable, type, syntax error
- Time required for cooking a refrigerated egg
- Python aliasing vs. copying variables
- Python script full of syntax errors
- Python script full of errors
- Python script full of errors
- Python dictionary of class members
- Python script call from the Bash command line
- Operator precedence
- Matrix Initialization
- MATLAB working directory
- MATLAB script full of errors
- Integer overflow
- Implementing the Bell-shaped (Gaussian) function
ugly
uncertainty quantification
unit-testing
urllib
usa
value
- value, variable, type, syntax error
- Time required for cooking a refrigerated egg
- Python aliasing vs. copying variables
- Python script full of syntax errors
- Python script full of errors
- Python script full of errors
- Python dictionary of class members
- Python script call from the Bash command line
- Operator precedence
- Matrix Initialization
- MATLAB working directory
- MATLAB script full of errors
- Integer overflow
- Implementing the Bell-shaped (Gaussian) function
variable
- value, variable, type, syntax error
- Time required for cooking a refrigerated egg
- Python aliasing vs. copying variables
- Python script full of syntax errors
- Python script full of errors
- Python script full of errors
- Python dictionary of class members
- Python script call from the Bash command line
- Operator precedence
- Matrix Initialization
- MATLAB working directory
- MATLAB script full of errors
- Integer overflow
- Implementing the Bell-shaped (Gaussian) function
variance
vectorization
venn
version control system
visualization
- Better visualizations through axes transformations
- Online comparison of the Kmeans clustering algorithm with DBSCAN
- Online experimentation with DBSCAN clustering technique
- Kmeans clustering - an implementation
- Kmeans clustering: Determining the cluster number using the Elbow method
- Regression: Predicting the distribution of the a dataset subjected to a smooth censorship (sample incompleteness)
- Regression: Predicting the global land temperature of Earth in 2050 from the past data: Choosing the best model
- Regression: Estimating the parameters of a linear model for a Normally-distributed sample
- Regression: Estimating the parameters of a Normally-distributed sample
- Ugly visualization
- The population growths of the US states
- The cities with the most and least moderate temperature
- Wrong visualization
- Excel Bar plot
- Visualization color scales
- Regression: Model selection for a bivariate data using Excel
- Visualizing and comparing the temperatures of Honolulu and Duluth
- Visualizing and comparing the temperatures of Honolulu and Duluth via Excel
- Visualizing the average precipitation of the US states vs. sunshine
- Visualizing the average precipitation among the US states
- Monte Carlo approximation of the number Pi
- Monte Carlo approximation of the number Pi using a full circle
- Monte Carlo approximation of the area of heart
- Parsing data from the World Wide Web
- Visualization: The world population
- Visualization: The world population (refined)
- Visualization: The world map resized by population
- Visualization: Worldwide Internet Usage
- Regression: Predicting the bivariate distribution of the a dataset subjected to censorship (sample incompleteness)
- Regression: Predicting the distribution of the a dataset subjected to censorship (sample incompleteness)
- Best visualization coloring
- Regression: Predicting the global land temperature of the Earth in 2050 from the past data via the maximum likelihood approach
- Regression: Predicting the global land temperature of the Earth in 2050 from the past data: linear vs. exponential temperature increase
- The different faces of binned data via different transformations
- Coordinates transformation for better visualization
- Regression: obtaining the most likely mean of a set of Standard Normally Distributed Random Variables
- Regression: obtaining the most likely mean of a set of Standard Normally Distributed Random Variables via the least absolute deviations method
- Regression: obtaining the most likely mean of a set of Standard Normally Distributed Random Variables via the least absolute deviations method
- Monte Carlo sampling of the sum of two Gaussian distributions
- Kmeans clustering
- Regression: Predicting the global land temperature of the Earth in 2050 from the past data
- Simulating the Monty Hall game
- Regression: obtaining the most likely mean and standard deviation of a set of Standard Normally Distributed Random Variables
- Reading data from the World Wide Web
- Getting the boundary of objects in images
- Monte Carlo sampling of distribution functions
- Monte Carlo approximation of the number Pi
- Subplots in MATLAB
warming
- Regression: Predicting the global land temperature of Earth in 2050 from the past data: Choosing the best model
- Computing the cross-correlation of two data attributes
- Computing the autocorrelation of a dataset
- Computing and removing the autocorrelation of a dataset
- Visualizing and comparing the temperatures of Honolulu and Duluth
- Visualizing and comparing the temperatures of Honolulu and Duluth via Excel
- Computing the covariance matrix of a dataset
- Computing the correlation matrix of a dataset
- Computing the Spearman rank correlation coefficient of a dataset
- Computing the Pearson correlation coefficient of a dataset
- The most sensitive correlation coefficient to outliers
- Computing the Kendall's rank correlation coefficient of a dataset
web
webpage
weighted
while-loop
who
whos
working directory
wrong
zeros
