Bayesian Modeling and Computation in Python

Last update: Jan 02, 2023

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Overview

Bayesian Modeling and Computation in Python

Open access and Code

This repository contains the open access version of the text and the code examples in the book. All of this can be more easily viewed at www.bayesiancomputationbook.com

See a mistake?

If you see any issues please create an issue on the issue tracker

Environment installation

To run the code you will need to install the correct packages in a computational environment. We have provided instructions below for common options.

Conda

conda env create -f environment.yml
conda activate bmcp

Colab

The book code can also be run using Google Colab. https://colab.research.google.com

More instructions to come soon

Owner

Bayesian Modeling and Computation in Python

Code, references and all material to accompany the text

GitHub Repository https://www.bayesiancomputationbook.com

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Bayesian Modeling and Computation in Python

Related tags

Overview

Bayesian Modeling and Computation in Python

Open access and Code

See a mistake?

Environment installation

Conda

Colab

Owner

Bayesian Modeling and Computation in Python

MLOps pipeline project using Amazon SageMaker Pipelines

Bayesian Modeling and Computation in Python

Machine learning model evaluation made easy: plots, tables, HTML reports, experiment tracking and Jupyter notebook analysis.

MosaicML Composer contains a library of methods, and ways to compose them together for more efficient ML training

Uplift modeling and causal inference with machine learning algorithms

Decentralized deep learning in PyTorch. Built to train models on thousands of volunteers across the world.

Repositório para o #alurachallengedatascience1

A single Python file with some tools for visualizing machine learning in the terminal.

Tools for diffing and merging of Jupyter notebooks.

Dual Adaptive Sampling for Machine Learning Interatomic potential.

Python implementation of the rulefit algorithm

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