Pinned Repositories
AoC
F# solution for AoC
F-99
Ninety-Nine F# Problems
mbmlbook
F# solution for mbml
parse_word_to_ppt_by_python
Probabilisitic-programming
A collection of [Microsoft Azure Notebooks](https://notebooks.azure.com/) ([Jupyter notebooks](http://jupyter.org/) hosted on [Azure](https://azure.microsoft.com/)) providing demonstrations of [probabilistic programming](https://www.oreilly.com/ideas/probabilistic-programming) using the following frameworks: \* [Infer.NET](http://infernet.azurewebsites.net/) *"Infer.NET is a framework for running Bayesian inference in graphical models. It can also be used for probabilistic programming"* **NOW OPEN SOURCE!** \* [Stan](http://mc-stan.org/) *"Stan is freedom-respecting, open-source software for facilitating statistical inference at the frontiers of applied statistics."* \* [PyMC](https://github.com/pymc-devs) (currently at PyMC3, with PyMC4 in the works) *"PyMC3 is a Python package for Bayesian statistical modeling and probabilistic machine learning which focuses on advanced Markov chain Monte Carlo and variational fitting algorithms."* \* [Edward](http://edwardlib.org/) *"A library for probabilistic modeling, inference, and criticism."* using the three supported languages of [python](https://www.python.org/), [R](https://www.r-project.org/) & [F\#](https://fsharp.org/).
usaco
C++ solution for https://train.usaco.org/
zangruizhe's Repositories
zangruizhe/parse_word_to_ppt_by_python
zangruizhe/AoC
F# solution for AoC
zangruizhe/mbmlbook
F# solution for mbml
zangruizhe/usaco
C++ solution for https://train.usaco.org/
zangruizhe/F-99
Ninety-Nine F# Problems
zangruizhe/Probabilisitic-programming
A collection of [Microsoft Azure Notebooks](https://notebooks.azure.com/) ([Jupyter notebooks](http://jupyter.org/) hosted on [Azure](https://azure.microsoft.com/)) providing demonstrations of [probabilistic programming](https://www.oreilly.com/ideas/probabilistic-programming) using the following frameworks: \* [Infer.NET](http://infernet.azurewebsites.net/) *"Infer.NET is a framework for running Bayesian inference in graphical models. It can also be used for probabilistic programming"* **NOW OPEN SOURCE!** \* [Stan](http://mc-stan.org/) *"Stan is freedom-respecting, open-source software for facilitating statistical inference at the frontiers of applied statistics."* \* [PyMC](https://github.com/pymc-devs) (currently at PyMC3, with PyMC4 in the works) *"PyMC3 is a Python package for Bayesian statistical modeling and probabilistic machine learning which focuses on advanced Markov chain Monte Carlo and variational fitting algorithms."* \* [Edward](http://edwardlib.org/) *"A library for probabilistic modeling, inference, and criticism."* using the three supported languages of [python](https://www.python.org/), [R](https://www.r-project.org/) & [F\#](https://fsharp.org/).