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

Building blocks for simple and advanced particle filtering in Gen.

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

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

Sequential Tree Sampler for online phylogenetics

Sequential Monte Carlo sampler for PyMC2 models.

pyABC: distributed, likelihood-free inference

Gradient-informed particle MCMC methods

Bayesian structure learning and classification in decomposable graphical models.

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).

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

R package serrsBayes

pocomc plugin for bilby

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

Implementation of Particle Smoothing Variational Objectives