Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
---|---|---|---|---|---|---|---|---|---|---|
Probability | 3,914 | 126 | 213 | 3 days ago | 43 | June 07, 2022 | 633 | apache-2.0 | Jupyter Notebook | |
Probabilistic reasoning and statistical analysis in TensorFlow | ||||||||||
Math Php | 2,222 | 36 | 22 | 9 days ago | 132 | April 10, 2022 | 51 | mit | PHP | |
Powerful modern math library for PHP: Features descriptive statistics and regressions; Continuous and discrete probability distributions; Linear algebra with matrices and vectors, Numerical analysis; special mathematical functions; Algebra | ||||||||||
Stat Cookbook | 2,102 | 3 months ago | 2 | other | TeX | |||||
:orange_book: The probability and statistics cookbook | ||||||||||
Www.mlcompendium.com | 1,991 | 3 days ago | ||||||||
The Machine Learning & Deep Learning Compendium was a list of references in my private & single document, which I curated in order to expand my knowledge, it is now an open knowledge-sharing project compiled using Gitbook. | ||||||||||
Ml Foundations | 1,705 | 7 months ago | mit | Jupyter Notebook | ||||||
Machine Learning Foundations: Linear Algebra, Calculus, Statistics & Computer Science | ||||||||||
Data Science Roadmap | 1,462 | 4 days ago | 1 | mit | ||||||
Data Science Roadmap from A to Z | ||||||||||
Hackermath | 1,339 | 6 years ago | 5 | mit | Jupyter Notebook | |||||
Introduction to Statistics and Basics of Mathematics for Data Science - The Hacker's Way | ||||||||||
Seeing Theory | 1,043 | 4 years ago | 22 | apache-2.0 | HTML | |||||
A visual introduction to probability and statistics. | ||||||||||
Distributions.jl | 992 | 766 | 20 hours ago | January 02, 2021 | 361 | other | Julia | |||
A Julia package for probability distributions and associated functions. | ||||||||||
Kotlin Statistics | 825 | 4 | 3 | a year ago | 7 | December 27, 2018 | 15 | apache-2.0 | Kotlin | |
Idiomatic statistical operators for Kotlin |
Teruo Nakatsuma (Faculty of Economics, Keio University, Japan)
notebook-a
notebook-b
I strongly recommend using Anaconda. It can install Python along with numerous essential packages at once and allows us to manage those packages flexibly.
If you have an older Anaconda on your PC, uninstall it completely by folloiwng instructions.
Download an Anaconda installer (Windows, macOS or Linux) from here. Choose an installer for your OS.
Doubleclick the installer and follow the instructions on the screen. Do not change the default settings.
Start Anaconda Powershell Prompt
(Windows) or Terminal
(macOS, Linux) and type
conda update conda
This will update conda (package manager) in Anaconda. Then type
conda create -n bayes -c conda-forge jupyterlab seaborn bokeh jupyter_bokeh pymc python-graphviz
This will create the environment for PyMC. Then type
conda activate bayes
and type
python -m ipykernel install --user --name bayes --display-name "Python (Bayes)"
Now you are ready for Python!
If you encounter any errors during the installation process, go back to the default environment by typing
conda deactivate
and remove bayes
by typing
conda env remove -n bayes
Then redo Step 2.
In case the computer says Command Line Tools for Xcode
is missing, install it as follows.
Install Xcode
from App Store.
Start Xcode
. If a pop-up window asks you to install additional tools, follow the instruction. Quit Xcode
.
Start Terminal
and install Command Line Tools for Xcode
by typing
sudo xcode-select --install
If asked, type your login password.
Start Anaconda Powershell Prompt
(Windows) or Terminal
(macOS, Linux) and type
conda activate bayes
Then type
jupyter notebook --port=8888
Your default browser will pop up.
Alternatively, you may use JupyerLab by typing
jupyter lab --port=8888
For a bokeh interactive plot to work properly, the Jupyter Notebook server must use port 8888
which is set by default. In case this port is occupied by another Jupyter Notebook server, you need to stop it by typing
jupyter notebook stop
before you open a new Jupyter Notebook. If this does not work, reboot your PC.
notebook-a
file name | description |
---|---|
Cholera.csv | London cholera pandemic data |
Mroz.csv | US women's labor participation data |
StrikeDur.csv | strikes duration data |
USStocksSW.csv | monthly US stock returns data |
ar1_process.ipynb | convergence of the AR(1) process |
cholera.ipynb | Bernoulli model of the cholera data |
example_bernoulli.ipynb | posterior inference on Bernoulli dist. |
example_exponential.ipynb | posterior inference on exponential dist. |
example_normal.ipynb | posterior inference on normal dist. |
example_poisson.ipynb | posterior inference on Poisson dist. |
Housing.csv | sales prices of houses |
housing_price.ipynb | hedonic price model of houses |
labor_participation.ipynb | logit model of labor participation |
logit.ipynb | PyMC example of logit model |
poisson_regression.ipynb | PyMC example of Poisson regression model |
probit.ipynb | PyMC example of probit model |
prussian.csv | Prussian army horse kick data |
regression.ipynb | PyMC example of regression analysis |
ships_damage.ipynb | Poisson regression model of ships damage |
ships.csv | ships damage data |
wage_education.ipynb | relationship between wage and education |
notebook-b
file name | description |
---|---|
bivariate_distribution.ipynb | examples of bivariate distributions |
large_sample.ipynb | consistency and asymptotic normality |
markovchain.ipynb | Markov chain |
probability_distribution.ipynb | examples of probability distributions |
python_introduction.ipynb | simple example of Bayes' theorem |
skewness_kurtosis.ipynb | skewness and kurtosis |