Probabilistic Modeling
Showing 23 of 23 projects
A flexible, scalable deep probabilistic programming library built on PyTorch for universal representation of computable probability distributions.
A flexible, scalable deep probabilistic programming library built on PyTorch for universal probabilistic modeling.
A highly efficient, scalable Gaussian process library implemented in PyTorch with GPU acceleration and modular design.
A Python library for probabilistic modeling built on PyTorch, offering modular distributions, GPU support, and flexible model composition.
An open-source Python library for probabilistic time series modeling with both frequentist and Bayesian inference methods.
A Python library for deep probabilistic modeling and analysis of single-cell and spatial omics data.
A Bayesian marketing analytics toolbox for Media Mix Modeling (MMM), Customer Lifetime Value (CLV), and customer choice analysis.
A Python library for probabilistic state space modeling and inference, built on JAX.
A low-level Gaussian process framework in JAX and Flax, designed for maximum flexibility and close alignment with mathematical notation.
A Python library for Bayesian inference in Hidden Markov Models (HMMs) and Hidden semi-Markov Models (HSMMs) with nonparametric extensions.
A Scala toolkit for deployable probabilistic modeling using imperatively-defined factor graphs.
A Python package providing Bayesian machine learning algorithms with a scikit-learn compatible API.
Python implementations of various topic modeling algorithms including LDA, collaborative topic models, and hierarchical Dirichlet processes.
A Python toolkit for probabilistic password guessing and analysis using Probabilistic Context-Free Grammar (PCFG) models.
A Python package for training PyTorch neural networks using variational inference for Bayesian deep learning.
An extremely lightweight Gaussian Process library for Python built on JAX with GPU acceleration and automatic differentiation.
A Julia package for Gaussian process modeling with support for exact inference, MCMC sampling, and non-Gaussian likelihoods.
A Python library for Bayesian optimization using GPflow and TensorFlow, designed for optimizing expensive black-box functions.
A JAX library for distributions, bijections, and normalizing flows implemented as Equinox modules.
A probabilistic cell segmentation method for spatial transcriptomics data from platforms like Xenium, CosMx, MERSCOPE, and Visium HD.
A lightweight Bayesian optimization library built on JAX for efficient optimization of expensive-to-evaluate functions.
A simple yet essential Python framework for Bayesian optimization, enabling efficient hyperparameter tuning and black-box function optimization.
A JAX library for building, training, and evaluating normalizing flows for probabilistic modeling.
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