Package (by Santeri Karppinen) for generic particle filtering and particle Markov chain Monte Carlo

Codes (with Santeri Karppinen) for experiments in the paper arXiv:2205.13898, about 'bridge backward sampling' generalisation of backward/ancestor sampling, which avoids degeneracy with models having weakly informative observations and stiff dynamics.

Codes for experiments in the paper arXiv:2203.10037, about performance of various resampling methods in the weakly informative regime.

Package (with Santeri Karppinen) which implements a number of unbiased resampling algorithms,which can be used easily within a particle filter (sequential Monte Carlo) algorithm.

Codes (by Santeri Karppinen) which implement the method for estimating animal territories and which was used in the experiments of the related paper doi:10.1016/j.ecolmodel.2022.110101.

Codes (by Santeri Karppinen) which implement the conditional particle filter with diffuse initialisation, and the experiments of the related paper doi:10.1007/s11222-020-09975-1/arXiv:2006.14877.

Package implementing random-walk based adaptive MCMC algorithms

Package implementing particle MCMC algorithms with adaptive proposals

Package (by Anthony Lee) implementing coupled conditional particle filters (for unbiased smoothing) suggested in doi:10.1214/19-AOS1922/arXiv:1806.05852.

Package which implements an adaptive tolerance ABC-MCMC with post-correction, as described in the related paper doi:10.1093/biomet/asz078/arXiv:1902.00412. See also abc-mcmc for the codes of the experiments in the paper.

R package (by Jouni Helske) for Bayesian Inference of State Space Models, implementing estimators described in doi:10.1111/sjos.12492/arXiv:1609.02541

Simple pure Python (& NumPy) implementation of the RAM algorithm

Matlab codes implementing the adaptive parallel tempering algorithm(s) on the experiments described in the related paper

Simple Matlab implementation of the RAM

C++ implementation of unbiased multilevel Monte Carlo estimators for diffusion expectations, as described in the paper

C implementation of several adaptive random-walk Metropolis algorithms on graphical models (or Bayesian networks), with models defined in Lua (and optionally partly in C)