Statistical-Rethinking-with-Python-and-PyMC3 - Python/PyMC3 port of the examples in " Statistical Rethinking A Bayesian Course with Examples in R and Stan" by Richard McElreath

Overview

Statistical Rethinking with Python and PyMC3

This repository has been deprecated in favour of this one, please check that repository for updates, for opening issues or sending pull requests

Statistical Rethinking is an incredible good introductory book to Bayesian Statistics, its follows a Jaynesian and practical approach with very good examples and clear explanations.

In this repository we ported the codes (originally in R and Stan) in the book to PyMC3. We are trying to keep the examples as close as possible to those in the book, while at the same time trying to express them in the most Pythonic and PyMC3onic way we can.

Display notebooks

View Jupyter notebooks in nbviewer

Contributing

If you want to contribute please, send your pull request to this. All contributions are welcome!

Installing the dependencies

To install the dependencies to run these notebooks, you can use Anaconda. Once you have installed Anaconda, run:

conda env create -f environment.yml

to install all the dependencies into an isolated environment. You can switch to this environment by running:

source activate stat-rethink-pymc3

Creative Commons License
Statistical Rethinking with Python and PyMC3 by All Contributors is licensed under a Creative Commons Attribution 4.0 International License.

Owner
Osvaldo Martin
Osvaldo Martin
Official Pytorch implementation for 2021 ICCV paper "Learning Motion Priors for 4D Human Body Capture in 3D Scenes" and trained models / data

Learning Motion Priors for 4D Human Body Capture in 3D Scenes (LEMO) Official Pytorch implementation for 2021 ICCV (oral) paper "Learning Motion Prior

165 Dec 19, 2022
CarND-LaneLines-P1 - Lane Finding Project for Self-Driving Car ND

Finding Lane Lines on the Road Overview When we drive, we use our eyes to decide where to go. The lines on the road that show us where the lanes are a

Udacity 769 Dec 27, 2022
Automatic self-diagnosis program (python required)Automatic self-diagnosis program (python required)

auto-self-checker 자동으로 자가진단 해주는 프로그램(python 필요) 중요 이 프로그램이 실행될때에는 절대로 마우스포인터를 움직이거나 키보드를 건드리면 안된다(화면인식, 마우스포인터로 직접 클릭) 사용법 프로그램을 구동할 폴더 내의 cmd창에서 pip

1 Dec 30, 2021
Fully Convlutional Neural Networks for state-of-the-art time series classification

Deep Learning for Time Series Classification As the simplest type of time series data, univariate time series provides a reasonably good starting poin

Stephen 572 Dec 23, 2022
A check for whether the dependency jobs are all green.

alls-green A check for whether the dependency jobs are all green. Why? Do you have more than one job in your GitHub Actions CI/CD workflows setup? Do

Re:actors 33 Jan 03, 2023
Source codes of CenterTrack++ in 2021 ICME Workshop on Big Surveillance Data Processing and Analysis

MOT Tracked object bounding box association (CenterTrack++) New association method based on CenterTrack. Two new branches (Tracked Size and IOU) are a

36 Oct 04, 2022
Replication package for the manuscript "Using Personality Detection Tools for Software Engineering Research: How Far Can We Go?" submitted to TOSEM

tosem2021-personality-rep-package Replication package for the manuscript "Using Personality Detection Tools for Software Engineering Research: How Far

Collaborative Development Group 1 Dec 13, 2021
Cross-modal Deep Face Normals with Deactivable Skip Connections

Cross-modal Deep Face Normals with Deactivable Skip Connections Victoria Fernández Abrevaya*, Adnane Boukhayma*, Philip H. S. Torr, Edmond Boyer (*Equ

72 Nov 27, 2022
Code of the paper "Deep Human Dynamics Prior" in ACM MM 2021.

Code of the paper "Deep Human Dynamics Prior" in ACM MM 2021. Figure 1: In the process of motion capture (mocap), some joints or even the whole human

Shinny cui 3 Oct 31, 2022
GUI for a Vocal Remover that uses Deep Neural Networks.

GUI for a Vocal Remover that uses Deep Neural Networks.

4.4k Jan 07, 2023
This repository is the official implementation of Unleashing the Power of Contrastive Self-Supervised Visual Models via Contrast-Regularized Fine-Tuning (NeurIPS21).

Core-tuning This repository is the official implementation of ``Unleashing the Power of Contrastive Self-Supervised Visual Models via Contrast-Regular

vanint 18 Dec 17, 2022
A general-purpose encoder-decoder framework for Tensorflow

READ THE DOCUMENTATION CONTRIBUTING A general-purpose encoder-decoder framework for Tensorflow that can be used for Machine Translation, Text Summariz

Google 5.5k Jan 07, 2023
The source code and data of the paper "Instance-wise Graph-based Framework for Multivariate Time Series Forecasting".

IGMTF The source code and data of the paper "Instance-wise Graph-based Framework for Multivariate Time Series Forecasting". Requirements The framework

Wentao Xu 24 Dec 05, 2022
Python script that allows you to automatically setup your Growtopia server.

AutoSetup Python script that allows you to automatically setup your Growtopia server. How To Use Firstly, install all the required modules that used i

Aspire 3 Mar 06, 2022
Sequence to Sequence (seq2seq) Recurrent Neural Network (RNN) for Time Series Forecasting

Sequence to Sequence (seq2seq) Recurrent Neural Network (RNN) for Time Series Forecasting Note: You can find here the accompanying seq2seq RNN forecas

Guillaume Chevalier 1k Dec 25, 2022
Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields.

This repository contains the code release for Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields. This implementation is written in JAX, and is a fork of Google's JaxNeRF

Google 625 Dec 30, 2022
Unofficial PyTorch implementation of MobileViT.

MobileViT Overview This is a PyTorch implementation of MobileViT specified in "MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Tr

Chin-Hsuan Wu 348 Dec 23, 2022
Fast and scalable uncertainty quantification for neural molecular property prediction, accelerated optimization, and guided virtual screening.

Evidential Deep Learning for Guided Molecular Property Prediction and Discovery Ava Soleimany*, Alexander Amini*, Samuel Goldman*, Daniela Rus, Sangee

Alexander Amini 75 Dec 15, 2022
Datasets and source code for our paper Webly Supervised Fine-Grained Recognition: Benchmark Datasets and An Approach

Introduction Datasets and source code for our paper Webly Supervised Fine-Grained Recognition: Benchmark Datasets and An Approach Datasets: WebFG-496

21 Sep 30, 2022