Sequential Monte Carlo in python

State estimation, smoothing and parameter estimation using Kalman and particle filters.

R package for statistical inference using partially observed Markov processes

Particle filtering and sequential parameter inference in Python

Sequential Monte Carlo algorithm for approximation of posterior distributions.

Implementation of advanced Sequential Monte Carlo and particle MCMC algorithms

Collected code and materials from the intensive course preparing for the workshop on Sequential Monte Carlo (SMC) methods at Uppsala University, August 2017

Building blocks for simple and advanced particle filtering in Gen.

Sequential Tree Sampler for online phylogenetics

This repo contains the code of Transitional Markov chain Monte Carlo algorithm

Sequential Monte Carlo sampler for PyMC2 models.

pyABC: distributed, likelihood-free inference

Bayesian structure learning and classification in decomposable graphical models.

Gradient-informed particle MCMC methods

A Bayesian uncertainty quantification toolbox for discrete and continuum numerical models of granular materials, developed by various projects of the University of Twente (NL), the Netherlands eScience Center (NL), University of Newcastle (AU), and Hiroshima University (JP).

R package serrsBayes

Automated Neuron Reconstruction from 3D Fluorescence Microscopy Images Using Sequential Monte Carlo Estimation

Implementation of Particle Smoothing Variational Objectives

An implementation of Neural Adaptive Sequential Monte Carlo (NASMC) using PyTorch

Variational Combinatorial Sequential Monte Carlo methods for Bayesian Phylogenetic Inference