Bayesian Modeling and Probabilistic Programming in Python
Deep universal probabilistic programming with Python and PyTorch
A Python implementation of global optimization with gaussian processes.
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.
Python Toolkit for Causal and Probabilistic Reasoning
Probabilistic programming with NumPy powered by JAX for autograd and JIT compilation to GPU/TPU/CPU.
Statistical package in Python based on Pandas
Gaussian processes in TensorFlow
Deep probabilistic analysis of single-cell and spatial omics data
Python-based research interface for blackbox and hyperparameter optimization, based on the internal Google Vizier Service.
High-quality implementations of standard and SOTA methods on a variety of tasks.
GemPy is an open-source, Python-based 3-D structural geological modeling software, which allows the implicit (i.e. automatic) creation of complex geological models from interface and orientation data. It also offers support for stochastic modeling to address parameter and model uncertainties.
BAyesian Model-Building Interface (Bambi) in Python.
Bayesian marketing toolbox in PyMC. Media Mix (MMM), customer lifetime value (CLV), buy-till-you-die (BTYD) models and more.
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
BlackJAX is a Bayesian Inference library designed for ease of use, speed and modularity.
sbi is a Python package for simulation-based inference, designed to meet the needs of both researchers and practitioners. Whether you need fine-grained control or an easy-to-use interface, sbi has you covered.
A Python library for simulation-based inference with deep learning
Methods to get the probability of a changepoint in a time series.
Gaussian processes in JAX and Equinox.
PyTensor allows you to define, optimize, and efficiently evaluate mathematical expressions involving multi-dimensional arrays.
Laplace approximations for Deep Learning.
Scientific hypothesis-discovery workbench: grounded hypothesis-evidence graphs, synthetic literature with planted causal chains, generate-debate-evolve loops over pluggable offline adapters, Elo tournaments recovering planted skill order, paraphrase dedup, Bayesian evidence accumulation, reproducible reports. NumPy core, CPU-only torch extra.
Bayesian Optimization and Design of Experiments
Sequential Monte Carlo in python
BidKing product source, self-hostable service components, and a standalone discrete-inference toolkit. MIT-licensed first-party code.
A Flipper Zero that stops guessing and starts reasoning: ML-guided RFID/NFC fuzzing & access-control auditing that turns raw card reads into a prioritized, explainable security assessment. Defensive-use only.