R package for statistical inference using partially observed Markov processes
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Updated
May 22, 2024 - R
R package for statistical inference using partially observed Markov processes
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
Code implementing Integrator Snippets, joint work with Christophe Andrieu and Chang Zhang
Implementation of advanced Sequential Monte Carlo and particle MCMC algorithms
Sequential Monte Carlo in python
Building blocks for simple and advanced particle filtering in Gen.
SEquential Analysis and Bayesian Experimental Design (SEABED) powered by JAX
State estimation, smoothing and parameter estimation using Kalman and particle filters.
Gradient-informed particle MCMC methods
Bayesian structure learning and classification in decomposable graphical models.
Variational Combinatorial Sequential Monte Carlo methods for Bayesian Phylogenetic Inference
Synthetic Data Generation by Sequential Monte Carlo (SMC)
Particle filtering and sequential parameter inference in Python
This module is an efficient and flexible implementation of various Sequential Monte Carlo (SMC) methods. Bayesian updates occur for both latent states and model parameters using joint inference.
Lightweight Metropolis Hasting as a rejuvenation procedures for particles in Sequential Monte Carlo. Inference in Higher Order Probabilistic Languages with Pytorch
This repo contains the code of Transitional Markov chain Monte Carlo algorithm
Example of an inverse problem where the aim is to reconstruct the parameters of an unknown number of weighted Gaussian function
Sequential Monte Carlo algorithm for approximation of posterior distributions.
Code for the paper "Backward importance sampling for online estimation of state space models"
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