Synthetic Data Generation by Sequential Monte Carlo (SMC)
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Updated
Aug 21, 2023 - MATLAB
Synthetic Data Generation by Sequential Monte Carlo (SMC)
Example of an inverse problem where the aim is to reconstruct the parameters of an unknown number of weighted Gaussian function
PhD dissertation: Methods for Automated Neuron Image Analysis candidate: Miroslav Radojevic Publisher: Erasmus University ISBN 978-94-6361-204-3
A Python package for likelihood-free inference (LFI) methods such as Approximate Bayesian Computation (ABC)
A variation of Pacman arcade game designed to train Pacman agents that use sensors to locate and eat invisible ghosts with phenomenal efficiency. Used Joint Particle Filter algorithm in AI to get 30% optimized results.
SEquential Analysis and Bayesian Experimental Design (SEABED) powered by JAX
Workshop for A Corunha in MCTS
This repository contains the Python modules and scripts to reproduce the results in the paper "Catanach, Vo, Munsky. IJUQ 2020."
Gradient-informed particle MCMC methods
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.
Compute partition functions.
A framework for particle identification and energy estimation using a sequential Monte Carlo method
Code for the paper "Backward importance sampling for online estimation of state space models"
Lightweight Metropolis Hasting as a rejuvenation procedures for particles in Sequential Monte Carlo. Inference in Higher Order Probabilistic Languages with Pytorch
Sequential Monte Carlo for Kinetic Prediction of Time-Varying Data Generating Processes
Code implementing Integrator Snippets, joint work with Christophe Andrieu and Chang Zhang
Automated Neuron Reconstruction from 3D Fluorescence Microscopy Images Using Sequential Monte Carlo Estimation
Implementation of Particle Smoothing Variational Objectives
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