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

ThunderGBM: Fast GBDTs and Random Forests on GPUs

Implemented four supervised learning Machine Learning algorithms

Price forecasting of SGB and IRFC Bonds and comparing there returns

Model factory is a ML training platform to help engineers to build ML models at scale

An open source framework that provides a simple, universal API for building distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyperparameter tuning library.

LinearRegression2 Tvads and CarSales

Bottleneck a collection of fast, NaN-aware NumPy array functions written in C.

Classification based on Fuzzy Logic(C-Means).

It is a forest of random projection trees

Backprop makes it simple to use, finetune, and deploy state-of-the-art ML models.

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