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BAyesian Model-Building Interface (Bambi) in Python.
The main features of bambinos/bambi are: Probabilistic Modeling.
Projects with overlapping indexed features include: amazaspshumik/sklearn-bayes — Python package for Bayesian Machine Learning with scikit-learn API. arviz-devs/arviz — Exploratory analysis of Bayesian models with Python. blei-lab/edward — Edward is a probabilistic programming language and inference engine designed for building deep generative models and… camdavidsonpilon/probabilistic-programming-and-bayesian-methods-for-hackers — This project is a computational statistics textbook and Bayesian data analysis course. It serves as a guide for… cornellius-gp/gpytorch — GPyTorch is a GPU-accelerated probabilistic framework and PyTorch library for implementing scalable Gaussian process… alan-turing-institute/skpro — A unified framework for tabular probabilistic regression, time-to-event prediction, and probability distributions in…
Python package for Bayesian Machine Learning with scikit-learn API
Exploratory analysis of Bayesian models with Python
Edward is a probabilistic programming language and inference engine designed for building deep generative models and Bayesian neural networks. It utilizes the TensorFlow framework to represent probabilistic models as differentiable computational graphs. The library enables the construction of complex data distributions through Bayesian neural networks, mixture models, and Gaussian processes. It differentiates itself by providing an integrated toolkit for both supervised and unsupervised probabilistic modeling, including the implementation of generative adversarial networks and mixture density
A unified framework for tabular probabilistic regression, time-to-event prediction, and probability distributions in python