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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/xu-yz19/spiked-matrix-models-with-structured-noise

공식 코드

Information limits and Thouless-Anderson-Palmer equations for spiked matrix models with structured noise

https://github.com/yale-nlp/bay-calibration-llm-evaluators

공식 코드

Bayesian Calibration of Win Rate Estimation with LLM Evaluators

https://github.com/yang-song/GASR

공식 코드

Bayesian Matrix Completion via Adaptive Relaxed Spectral Regularization

https://github.com/yannadani/vcn_pytorch

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Variational Causal Networks: Approximate Bayesian Inference over Causal Structures

https://github.com/yao-yl/Multimodal-stacking-code

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Stacking for Non-mixing Bayesian Computations: The Curse and Blessing of Multimodal Posteriors

https://github.com/yao-yl/hierarchical-stacking-code

공식 코드

Bayesian hierarchical stacking: Some models are (somewhere) useful

https://github.com/yifei-xiong/efficient-snpe

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An efficient likelihood-free Bayesian inference method based on sequential neural posterior estimation

https://github.com/yifei-xiong/simulation-based-bayesian-inference-from-privacy-protected-data

공식 코드

Simulation-based Bayesian Inference from Privacy Protected Data

https://github.com/yihshe/MAGIC

공식 코드

MAGIC: Modular Auto-encoder for Generalisable Model Inversion with Bias Corrections

https://github.com/ymlasu/PeSBL_PDE_Learning

공식 코드

Parsimony-Enhanced Sparse Bayesian Learning for Robust Discovery of Partial Differential Equations

https://github.com/yongchaohuang/eparvi

공식 코드

Electrostatics-based particle sampling and approximate inference

https://github.com/yorkerlin/VB-MixEF

공식 코드

Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family Approximations

https://github.com/yufeiwu1011/Robust-Amortized-Bayesian-Inference

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Testing and Improving the Robustness of Amortized Bayesian Inference for Cognitive Models

https://github.com/yugahikida/multilevel-sbi

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Multilevel neural simulation-based inference

https://github.com/yunangao/basofr

공식 코드

Bayesian adaptive and interpretable functional regression for exposure profiles

https://github.com/yunchuankong/SharedClustering

공식 코드

A Bayesian Method for Joint Clustering of Vectorial Data and Network Data

https://github.com/yuzhiyang123/channel-diffusion

공식 코드

Generative Diffusion Receivers: Achieving Pilot-Efficient MIMO-OFDM Communications

https://github.com/yyssl88/bclean

공식 코드

BClean: A Bayesian Data Cleaning System

https://github.com/zankin/sovbr

공식 코드

Sparse online variational Bayesian regression

https://github.com/zguo235/bayesian_retro

공식 코드

A Bayesian algorithm for retrosynthesis

https://github.com/zhiyuzz/Bayesian-Conformal

공식 코드

The Benefit of Being Bayesian in Online Conformal Prediction

https://github.com/zhoufeng6288/hgcox

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Heterogeneous Multi-Task Gaussian Cox Processes

https://github.com/ziq-ao/GradEIG

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On Estimating the Gradient of the Expected Information Gain in Bayesian Experimental Design

https://github.com/zju-xwk/sla-vbi

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Successive Linear Approximation VBI for Joint Sparse Signal Recovery and Dynamic Grid Parameters Estimation

https://github.com/zongrenzou/hjpde4uqsciml

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Leveraging viscous Hamilton-Jacobi PDEs for uncertainty quantification in scientific machine learning

https://github.com/zovialpapai/d-betel

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Robust probabilistic inference via a constrained transport metric

https://github.com/zuhengxu/Consistent-Stochastic-Variational-Inference

공식 코드

The computational asymptotics of Gaussian variational inference and the Laplace approximation

https://github.com/zyy123jy/kdd

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A Probabilistic Disease Progression Model for Predicting Future Clinical Outcome

https://github.com/zzzx1224/A-Bit-More-Bayesian

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A Bit More Bayesian: Domain-Invariant Learning with Uncertainty

https://github.com/zzzx1224/singlesamplegeneralization-iclr2022

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Learning to Generalize across Domains on Single Test Samples

https://gitlab.com/desupervised/borch

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Borch: A Deep Universal Probabilistic Programming Language

https://gitlab.com/drti/kernax

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Kernel Stein Discrepancy thinning: a theoretical perspective of pathologies and a practical fix with regularization

https://gitlab.com/eijikawasaki/double-pendulum

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Data Subsampling for Bayesian Neural Networks

https://gitlab.com/lambda-hse/nfad

공식 코드

NFAD: Fixing anomaly detection using normalizing flows

https://plmlab.math.cnrs.fr/stoch-algo-phys/generative-models/fixed-kinetic-NHF/

공식 코드

Fixed-kinetic Neural Hamiltonian Flows for enhanced interpretability and reduced complexity

https://renkulab.io/gitlab/bistom/enca-inca

공식 코드

Learning Summary Statistics for Bayesian Inference with Autoencoders

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