JuliaStats/HiddenMarkovModels.jl

StateSpaceModels.jl is a Julia package for time-series analysis using state-space models.

A Python package for probabilistic state space modeling with JAX

MDPs and POMDPs in Julia - An interface for defining, solving, and simulating fully and partially observable Markov decision processes on discrete and continuous spaces.

Types and utility functions for summarizing Markov chain Monte Carlo simulations

Bayesian inference with probabilistic programming.

Implementation of robust dynamic Hamiltonian Monte Carlo methods (NUTS) in Julia.

Extension functionality which uses Stan.jl, DynamicHMC.jl, and Turing.jl to estimate the parameters to differential equations and perform Bayesian probabilistic scientific machine learning

A Bayesian Analysis Toolkit in Julia

R package for statistical inference using partially observed Markov processes