Statistical Rethinking: A Bayesian Course Using CmdStanPy and Plotnine

Overview

Statistical Rethinking: A Bayesian Course Using CmdStanPy and Plotnine

Intro

This repo contains the python/stan version of the Statistical Rethinking course that Professor Richard McElreath taught on the Max Planck Institute for Evolutionary Anthropology in Leipzig during the Winter of 2019/2020. The original repo for the course, from which this repo is forked, can be found here. The course contains 20 lectures structured in 10 weeks with a series of assignments for each week. The course is an excellent introduction to bayesian modelling in general and to the Rethinking Statistics wonderful book written by Professor McElreath.

How to use this repo

There are ten jupyter notebooks, one for each week of the course. At the beginning of each notebook there are links to the youtube videos of the lectures, the slides used and the original homework questions and answers in R.

How I would use this repo is like this:

  1. Go to the notebook of the week.
  2. Watch the two videos for the lectures of that week. Their URL are at the very top of each notebook.
  3. Read the original problems presented to the students and try to solve them on your own.
  4. Follow the exercises solutions of the notebook with my code and explanations by Professor McElreath.

Installing CmdStanPy

The stan code is executed thanks to CmdStanPy. CmdStanPy is a lightweight pure-Python interface to CmdStan which provides access to the Stan compiler and all inference algorithms. It provides the function install_cmdstan() which downloads CmdStan from GitHub and builds the CmdStan utilities. It can be can be called from within Python or from the command line.

import cmdstanpy
cmdstanpy.install_cmdstan()

You can found more information about the installation process here.

Other useful resources

There are a lot of very useful resources for bayesian statistical modelling out there. Specifically centered on Professor McElreath work I would mention:

  1. Original repo for the course.
  2. Original rethinking package repo

Copyright

The present work is a derivative work of Statistical Rethinking: A Bayesian Course Using python and pymc3 by Gabriel Bosque Chacon and Statistical Rethinking: A Bayesian Course Using Python and NumPyro by Andrés Suárez. I made the stan code, the plotnine figures and slightly modifications to his comments.

Owner
Andrés Suárez
Just a curious mind.
Andrés Suárez
BinTuner is a cost-efficient auto-tuning framework, which can deliver a near-optimal binary code that reveals much more differences than -Ox settings.

BinTuner is a cost-efficient auto-tuning framework, which can deliver a near-optimal binary code that reveals much more differences than -Ox settings. it also can assist the binary code analysis rese

BinTuner 42 Dec 16, 2022
track your GitHub statistics

GitHub-Stalker track your github statistics 👀 features find new followers or unfollowers find who got a star on your project or remove stars find who

Bahadır Araz 34 Nov 18, 2022
Projeto para realizar o RPA Challenge . Utilizando Python e as bibliotecas Selenium e Pandas.

RPA Challenge in Python Projeto para realizar o RPA Challenge (www.rpachallenge.com), utilizando Python. O objetivo deste desafio é criar um fluxo de

Henrique A. Lourenço 1 Apr 12, 2022
Probabilistic reasoning and statistical analysis in TensorFlow

TensorFlow Probability TensorFlow Probability is a library for probabilistic reasoning and statistical analysis in TensorFlow. As part of the TensorFl

3.8k Jan 05, 2023
This is an example of how to automate Ridit Analysis for a dataset with large amount of questions and many item attributes

This is an example of how to automate Ridit Analysis for a dataset with large amount of questions and many item attributes

Ishan Hegde 1 Nov 17, 2021
Toolchest provides APIs for scientific and bioinformatic data analysis.

Toolchest Python Client Toolchest provides APIs for scientific and bioinformatic data analysis. It allows you to abstract away the costliness of runni

Toolchest 11 Jun 30, 2022
Larch: Applications and Python Library for Data Analysis of X-ray Absorption Spectroscopy (XAS, XANES, XAFS, EXAFS), X-ray Fluorescence (XRF) Spectroscopy and Imaging

Larch: Data Analysis Tools for X-ray Spectroscopy and More Documentation: http://xraypy.github.io/xraylarch Code: http://github.com/xraypy/xraylarch L

xraypy 95 Dec 13, 2022
Vectorizers for a range of different data types

Vectorizers for a range of different data types

Tutte Institute for Mathematics and Computing 69 Dec 29, 2022
A Python package for modular causal inference analysis and model evaluations

Causal Inference 360 A Python package for inferring causal effects from observational data. Description Causal inference analysis enables estimating t

International Business Machines 506 Dec 19, 2022
Candlestick Pattern Recognition with Python and TA-Lib

Candlestick-Pattern-Recognition-with-Python-and-TA-Lib Goal Look at the S&P500 to try and get a better understanding of these candlestick patterns and

Ganesh Jainarain 11 Oct 07, 2022
An experimental project I'm undertaking for the sole purpose of increasing my Python knowledge

5ePy is an experimental project I'm undertaking for the sole purpose of increasing my Python knowledge. #Goals Goal: Create a working, albeit lightwei

Hayden Covington 1 Nov 24, 2021
A set of functions and analysis classes for solvation structure analysis

SolvationAnalysis The macroscopic behavior of a liquid is determined by its microscopic structure. For ionic systems, like batteries and many enzymes,

MDAnalysis 19 Nov 24, 2022
MEAD: A Large-scale Audio-visual Dataset for Emotional Talking-face Generation [ECCV2020]

MEAD: A Large-scale Audio-visual Dataset for Emotional Talking-face Generation [ECCV2020] by Kaisiyuan Wang, Qianyi Wu, Linsen Song, Zhuoqian Yang, Wa

112 Dec 28, 2022
Data Analytics on Genomes and Genetics

Data Analytics performed on On genomes and Genetics dataset to predict genetic disorder and disorder subclass. DONE by TEAM SIGMA!

1 Jan 12, 2022
A distributed block-based data storage and compute engine

Nebula is an extremely-fast end-to-end interactive big data analytics solution. Nebula is designed as a high-performance columnar data storage and tabular OLAP engine.

Columns AI 131 Dec 26, 2022
Reading streams of Twitter data, save them to Kafka, then process with Kafka Stream API and Spark Streaming

Using Streaming Twitter Data with Kafka and Spark Reading streams of Twitter data, publishing them to Kafka topic, process message using Kafka Stream

Rustam Zokirov 1 Dec 06, 2021
Python reader for Linked Data in HDF5 files

Linked Data are becoming more popular for user-created metadata in HDF5 files.

The HDF Group 8 May 17, 2022
PandaPy has the speed of NumPy and the usability of Pandas 10x to 50x faster (by @firmai)

PandaPy "I came across PandaPy last week and have already used it in my current project. It is a fascinating Python library with a lot of potential to

Derek Snow 527 Jan 02, 2023
Convert tables stored as images to an usable .csv file

Convert an image of numbers to a .csv file This Python program aims to convert images of array numbers to corresponding .csv files. It uses OpenCV for

711 Dec 26, 2022
Bamboolib - a GUI for pandas DataFrames

Community repository of bamboolib bamboolib is joining forces with Databricks. For more information, please read our announcement. Please note that th

Tobias Krabel 863 Jan 08, 2023