Open-source alternatives to Numpyro

PyStan, a Python interface to Stan, a platform for statistical modeling. Documentation: https://pystan.readthedocs.io

Fast, flexible and easy to use probabilistic modelling in Python.

Exploratory analysis of Bayesian models with Python

TensorFlow Probability is a library for probabilistic reasoning and statistical analysis integrated with the TensorFlow ecosystem. It serves as a Bayesian deep learning framework, a probabilistic programming interface, and a variational inference engine, providing a toolset for Markov chain Monte Carlo sampling and tensor-based probabilistic modeling. The project enables the construction of neural networks with probabilistic weights and the implementation of Bayesian neural networks to quantify prediction uncertainty. It provides specialized capabilities for hierarchical probabilistic modelin

PyMC is a Bayesian probabilistic programming framework used for building probabilistic models and performing Bayesian inference. It provides a probabilistic graphical model library for specifying random variables, priors, and likelihood functions, supported by an MCMC sampling engine and variational inference tools to estimate posterior distributions. The framework features a GPU-accelerated inference backend that compiles models into machine code to increase execution speed. It utilizes a backend-agnostic tensor execution model and just-in-time graph compilation to optimize the computation o

Python Toolkit for Causal and Probabilistic Reasoning

Prophet is a predictive analytics framework and time series regression library designed for forecasting future values. It uses additive models to fit non-linear growth and periodic seasonal patterns, providing tools for producing forecasts with integrated error measurement. The project handles multiple seasonalities and holiday effects to improve accuracy for periodic data. It supports the integration of external regressors and manages data irregularities, such as missing data and outliers, to maintain prediction stability. The framework covers a broad range of analysis capabilities, includi

This project is a Python data analysis library and exploratory data analysis framework designed for processing raw datasets. It provides a suite of tools for examining data, identifying anomalies, and applying statistical methods to uncover patterns. The repository functions as a machine learning modeling toolkit and a statistical data modeling suite. It includes predictive algorithms and mathematical models used to analyze relationships between data variables and derive insights from complex datasets. The project covers a broad range of capabilities including data science, machine learning

This project is a scientific computing framework for the .NET ecosystem, providing a comprehensive suite of libraries for numerical analysis, statistics, and mathematical optimization. It serves as a foundational toolkit for developing applications in machine learning, digital signal processing, and computer vision. The framework provides specialized toolkits for training and deploying predictive models, including neural networks, support vector machines, and decision trees. It further distinguishes itself with deep integrations for real-time visual analysis, such as object tracking and facia

This project is a comprehensive collection of practical code examples and implementation libraries for machine learning. It provides a wide array of reference materials for building supervised, unsupervised, and reinforcement learning algorithms. The repository serves as a multi-domain resource, featuring specific implementation suites for financial AI, Bayesian statistical modeling, and deep learning architectures. It includes a framework for training intelligent agents using policy gradients and actor-critic models, as well as practical guides for fine-tuning transformers and utilizing larg

This project is a synthetic data generator designed to create realistic tabular and time-series datasets for machine learning and testing workflows. It functions as a privacy-preserving platform that models the underlying statistical distributions of source data to produce new records that maintain the original statistical properties and structural integrity. The tool distinguishes itself by utilizing CPU-optimized statistical sampling, allowing for high-performance data generation on standard hardware without the need for specialized graphics processing units. It employs a configuration-driv

This repository serves as an educational resource and structured curriculum for performing statistical analysis using Python. It provides a comprehensive guide to the scientific computing workflow, focusing on the practical application of data cleaning, numerical modeling, and distribution visualization. The tutorial covers the end-to-end process of transforming raw tabular data into actionable insights. It demonstrates how to manipulate structured datasets through merging and aggregation, perform descriptive and inferential statistical calculations, and fit regression models to evaluate rela

The Python ensemble sampling toolkit for affine-invariant MCMC

the tiniest of Gaussian Process libraries

CONTRIBUTORS WELCOME Generalized Additive Models in Python

GPyTorch is a GPU-accelerated probabilistic framework and PyTorch library for implementing scalable Gaussian process models. It provides a system for Gaussian process modeling and uncertainty estimation, designed to perform efficient matrix operations on graphics hardware. The framework features a modular kernel system for constructing custom covariance functions and modeling complex data dependencies. It specifically integrates Gaussian processes with deep neural networks to create hybrid models for regression and classification. The system employs numerical linear algebra techniques, inclu

A unified framework for tabular probabilistic regression, time-to-event prediction, and probability distributions in python

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Documentation: Stable, Latest.

Main Status:

A library for hidden semi-Markov models with explicit durations

Approximate inference for Markov (i.e., temporal) Gaussian processes using iterated Kalman filtering and smoothing. Developed and maintained by William Wilkinson. The Bernoulli likelihood was implemented by Paul Chang. We are based in Arno Solin's machine learning group at Aalto University, Finland.

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Multiple Pairwise Comparisons (Post Hoc) Tests in Python

InferPy: Deep Probabilistic Modeling with Tensorflow Made Easy

Survival analysis in Python