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Bayesian Inference

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보존된 평가 기록: 2

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/RuiZhang2016/Quantile-Propagation-for-Wasserstein-Approximate-Gaussian-Processes

공식 코드

Quantile Propagation for Wasserstein-Approximate Gaussian Processes

https://github.com/Runjing-Liu120/DeblendingStarfields

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Variational Inference for Deblending Crowded Starfields

https://github.com/SciemusGithub/FBL

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Fuzzy Bayesian Learning

https://github.com/SmartImpulse/Wonterfact

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Probabilistic semi-nonnegative matrix factorization: a Skellam-based framework

https://github.com/SwissTPH/openmalaria

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Simulation-Based Inference for Global Health Decisions

https://github.com/TeaPearce/Bayesian_NN_Ensembles

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Uncertainty in Neural Networks: Approximately Bayesian Ensembling

https://github.com/TeemuSailynoja/posterior-sbc

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Posterior SBC: Simulation-Based Calibration Checking Conditional on Data

https://github.com/ThomasBrouwer/BNMTF_ARD

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Comparative Study of Inference Methods for Bayesian Nonnegative Matrix Factorisation

https://github.com/Tom-Ryder/VIforSSMs

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The Neural Moving Average Model for Scalable Variational Inference of State Space Models

https://github.com/TuringLang/DynamicPPL.jl

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DynamicPPL: Stan-like Speed for Dynamic Probabilistic Models

https://github.com/VBayesLab/Tutorial-on-VB

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A practical tutorial on Variational Bayes

https://github.com/VLL-HD/HINT

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HINT: Hierarchical Invertible Neural Transport for Density Estimation and Bayesian Inference

https://github.com/VMBoehm/PAE

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Probabilistic Autoencoder

https://github.com/ViktorAJStein/Accelerated_Stein_Variational_Gradient_Flows

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Accelerated Stein Variational Gradient Flow

https://github.com/WardHaddadin/Invariant-polynomials-and-machine-learning

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Invariant polynomials and machine learning

https://github.com/Wenlin-Chen/DiGS

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Diffusive Gibbs Sampling

https://github.com/XanderJC/scalable-birl

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Scalable Bayesian Inverse Reinforcement Learning

https://github.com/XinyiYS/Parametric-Bayesian-Learning-Games

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On the Convergence of the Shapley Value in Parametric Bayesian Learning Games

https://github.com/YiifeiWang/Accelerated-Information-Gradient-flow

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Accelerated Information Gradient flow

https://github.com/Zhengyu-Huang/InverseProblems.jl

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Efficient, Multimodal, and Derivative-Free Bayesian Inference With Fisher-Rao Gradient Flows

https://github.com/ZhuangLab/storm-analysis

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Teaching deep neural networks to localize single molecules for super-resolution microscopy

https://github.com/aadityasingh/icl-transience

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The Transient Nature of Emergent In-Context Learning in Transformers

https://github.com/aalto-intelligent-robotics/bayesianfloorfield

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Bayesian Floor Field: Transferring people flow predictions across environments

https://github.com/aasensio/bayesdi

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Approximate Bayesian Neural Doppler Imaging

https://github.com/abacusai/forecastpfn

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ForecastPFN: Synthetically-Trained Zero-Shot Forecasting

https://github.com/acasamitjana/3dhirest

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Robust joint registration of multiple stains and MRI for multimodal 3D histology reconstruction: Application to the Allen human brain atlas

https://github.com/acasamitjana/uslr

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USLR: an open-source tool for unbiased and smooth longitudinal registration of brain MR

https://github.com/acerbilab/pyvbmc

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PyVBMC: Efficient Bayesian inference in Python

https://github.com/acerbilab/vsbq

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Fast post-process Bayesian inference with Variational Sparse Bayesian Quadrature

https://github.com/adjidieng/REM

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Reweighted Expectation Maximization

https://github.com/adockhorn/markovsenior

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Markov Senior -- Learning Markov Junior Grammars to Generate User-specified Content

https://github.com/adriencorenflos/particle_mala

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Particle-MALA and Particle-mGRAD: Gradient-based MCMC methods for high-dimensional state-space models

https://github.com/ageorgou/roulette

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Unbiased Bayesian Inference for Population Markov Jump Processes via Random Truncations

https://github.com/ai-submit/optimalwasserstein

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Optimal Neural Network Approximation of Wasserstein Gradient Direction via Convex Optimization

https://github.com/aicherc/sgmcmc_ssm_code

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Stochastic Gradient MCMC for State Space Models

https://github.com/ajboyd2/pytorch_lvi

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Structured Stochastic Gradient MCMC

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.