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
Hashed Lookup Table based Matrix Multiplication (halutmatmul) - Stella Nera accelerator
A Python package for approximate Bayesian inference and optimization using Gaussian processes
A primer on Bayesian Neural Networks. The aim of this reading list is to facilitate the entry of new researchers into the field of Bayesian Deep Learning, by providing an overview of key papers. More details: "A Primer on Bayesian Neural Networks: Review and Debates"
Probabilistic Programming with Gaussian processes in Julia
Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more
Input Inference for Control (i2c), a control-as-inference framework for optimal control
Simulation-based inference using SSNL
Codes for 'Improving Hyperparameter Learning under Approximate Inference in Gaussian Process Models' (ICML 2023)
Expectation Maximisation, Variational Bayes, ARD, Loopy Belief Propagation, Gaussian Process Regression
FAIKR MOD3 project
DGMs for NLP. A roadmap.
Code repository for the paper No-Regret Approximate Inference via Bayesian Optimisation, published at UAI 2021
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.
Benchmark of posterior and model inference algorithms for (moderately) expensive likelihoods.
STOT: Single-Target Object Tracking using particle and Kalman filters [with a bonus multi-target].
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.
PyTorch implementation for "Probabilistic Circuits for Variational Inference in Discrete Graphical Models", NeurIPS 2020
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