Text and code for the forthcoming second edition of Think Bayes, by Allen Downey.

Last update: Jan 08, 2023

Overview

Think Bayes 2

by Allen B. Downey

Think Bayes is an introduction to Bayesian statistics using computational methods.

Think Bayes is a Free Book. It is available under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0), which means that you are free to copy and modify it, as long as you attribute the work and don’t use it for commercial purposes.

Other Free Books by Allen Downey are available from Green Tea Press.

Run the notebooks

Download the notebooks as a Zip file

Or use these links to run the notebooks on Colab:

Owner

Allen Downey

Professor at Olin College, author of Think Python, Think Bayes, Think Stats, and other books. Blog author of Probably Overthinking It.

GitHub Repository http://allendowney.github.io/ThinkBayes2/

A heterogeneous entity-augmented academic language model based on Open Academic Graph (OAG)

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Text and code for the forthcoming second edition of Think Bayes, by Allen Downey.

Related tags

Overview

Think Bayes 2

Run the notebooks

Owner

Allen Downey

A heterogeneous entity-augmented academic language model based on Open Academic Graph (OAG)

CoaT: Co-Scale Conv-Attentional Image Transformers

2D Time independent Schrodinger equation solver for arbitrary shape of well

Single Image Super-Resolution (SISR) with SRResNet, EDSR and SRGAN

Memory efficient transducer loss computation

This is project is the implementation of the DeepShift: Towards Multiplication-Less Neural Networks paper

Official PyTorch implementation of "IntegralAction: Pose-driven Feature Integration for Robust Human Action Recognition in Videos", CVPRW 2021

Large scale and asynchronous Hyperparameter Optimization at your fingertip.

Learning from Synthetic Humans, CVPR 2017

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A small demonstration of using WebDataset with ImageNet and PyTorch Lightning

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