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Bayesian Inference

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Bayesian Inference is a methodology that employs Bayes Rule to estimate parameters (and their full posterior).

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/kesslerlib/kessler

Spacecraft Collision Risk Assessment with Probabilistic Programming

https://github.com/kirthevasank/mps

Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic Programming

https://github.com/kmatt10/DNNGP

Deep Neural Networks as Gaussian Processes

https://github.com/kosyoshida/simple-keras

Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

https://github.com/kumar-shridhar/BayesianConvNet

Weight Uncertainty in Neural Networks

https://github.com/kushagragpt99/MCMC

Bayesian inference for logistic models using Polya-Gamma latent variables

https://github.com/lamantinushka/StructuredCovariance

A Simple Baseline for Bayesian Uncertainty in Deep Learning

https://github.com/larslorch/dibs

Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm

https://github.com/leequant761/Gumbel-SSVAE

Semi-Supervised Learning with Deep Generative Models

https://github.com/lightning-uq-box/lightning-uq-box

A Simple Baseline for Bayesian Uncertainty in Deep Learning

https://github.com/liqichen6688/baycnn

Weight Uncertainty in Neural Networks

https://github.com/longinyu/ksivi

Kernel Semi-Implicit Variational Inference

https://github.com/lpodl/Stein-Variational-Gradient-Descend

Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm

https://github.com/lucadellalib/bayestorch

Weight Uncertainty in Neural Networks

https://github.com/lucadellalib/bdl-rul-svgd

Weight Uncertainty in Neural Networks

https://github.com/lukasadam/piVAE

$π$VAE: a stochastic process prior for Bayesian deep learning with MCMC

https://github.com/luke-97/BayesByBackprop

Weight Uncertainty in Neural Networks

https://github.com/lupalab/flowscan

BRUNO: A Deep Recurrent Model for Exchangeable Data

https://github.com/magahub/songrnn

A Theoretically Grounded Application of Dropout in Recurrent Neural Networks

https://github.com/marcovirgolin/UncertaintyEstimationInDeepNets

Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

https://github.com/martin-gorner/tensorflow-rnn-shakespeare

A Theoretically Grounded Application of Dropout in Recurrent Neural Networks

https://github.com/matyushinleonid/WeightPrior

The Deep Weight Prior

https://github.com/mbaltieri/GeneralisedFiltering

On Kalman-Bucy filters, linear quadratic control and active inference

https://github.com/mcqll/mcqll.github.io

Grammar Induction for Minimalist Grammars using Variational Bayesian Inference : A Technical Report

https://github.com/mengxiangming/qcs-sgm

QCM-SGM+: Improved Quantized Compressed Sensing With Score-Based Generative Models

https://github.com/mhw32/variational-item-response-theory-public

Variational Item Response Theory: Fast, Accurate, and Expressive

https://github.com/microsoft/recommenders

Variational Autoencoders for Collaborative Filtering

https://github.com/microsoft/ticl

TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second

https://github.com/mijungi/vips_code

Variational Bayes In Private Settings (VIPS)

https://github.com/mj-will/pocomc-bilby

Persistent Sampling: Enhancing the Efficiency of Sequential Monte Carlo

https://github.com/mjt16/msci-multinest

MultiNest: an efficient and robust Bayesian inference tool for cosmology and particle physics

https://github.com/mkfilipiuk/VAE-CF

Variational Autoencoders for Collaborative Filtering

https://github.com/mlpc-ucsd/bdm

Bayesian Diffusion Models for 3D Shape Reconstruction

https://github.com/mnonnenm/SNL_py3port

Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows

https://github.com/mrahtz/learning-from-human-preferences

Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

https://github.com/mrquincle/noparama

The Informed Sampler: A Discriminative Approach to Bayesian Inference in Generative Computer Vision Models

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