Implementation of Prior, Rejection, Likelihood and Gibbs Sampling
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Updated
Feb 4, 2024 - Python
Implementation of Prior, Rejection, Likelihood and Gibbs Sampling
Correcting predictions for approximate Bayesian inference
Code repository for the paper No-Regret Approximate Inference via Bayesian Optimisation, published at UAI 2021
This project implements both exact and approximate inference techniques for Bayesian Networks using enumeration and rejection sampling, respectively. It processes Bayesian Network structures in XMLBIF format, accepting command-line inputs to compute the posterior distribution of a query variable given observed evidence.
Denoise a given image using Loopy Belief Propagation
Empirical analysis of recent stochastic gradient methods for approximate inference in Bayesian deep learning, including SWA-Gaussian, MultiSWAG, and deep ensembles. See report_localglobal.pdf.
STOT: Single-Target Object Tracking using particle and Kalman filters [with a bonus multi-target].
Expectation Maximisation, Variational Bayes, ARD, Loopy Belief Propagation, Gaussian Process Regression
Simulation-based inference using SSNL
Code repository for the UAI 2020 paper "Active learning of conditional mean embeddings via Bayesian optimisation" by S. R. Chowdhury, R. Oliveira and F. Ramos.
An implementation of loopy belief propagation for binary image denoising. Both sequential and parallel updates are implemented.
My undergraduate honours project, with others' private information/code removed.
Probabilistic approach to neural nets - modern scalable approximate inference methods
PyTorch implementation for "Probabilistic Circuits for Variational Inference in Discrete Graphical Models", NeurIPS 2020
Approximate Ridge Linear Mixed Models (arLMM)
FAIKR MOD3 project
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