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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/izmailovpavel/bnn_covariate_shift

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Dangers of Bayesian Model Averaging under Covariate Shift

https://github.com/jacksimons15327/snpse_icml

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Sequential Neural Score Estimation: Likelihood-Free Inference with Conditional Score Based Diffusion Models

https://github.com/jackyzyb/bayesian-coresets-optimization

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Bayesian Coresets: Revisiting the Nonconvex Optimization Perspective

https://github.com/jakesnell/neural-circuits

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A Metalearned Neural Circuit for Nonparametric Bayesian Inference

https://github.com/jbaker92/scir

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Large-Scale Stochastic Sampling from the Probability Simplex

https://github.com/jdrugo/vb_linear

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Variational Bayesian inference for linear and logistic regression

https://github.com/jeremyalain/imitation_learning_from_language_feedback

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Training Language Models with Language Feedback at Scale

https://github.com/jglaubitz/leveragingjointsparsity

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Leveraging joint sparsity in hierarchical Bayesian learning

https://github.com/jhuggins/viabel

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Validated Variational Inference via Practical Posterior Error Bounds

https://github.com/jiang-hb/vbreg

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Robust Outlier Rejection for 3D Registration with Variational Bayes

https://github.com/jjfeng/monitoring_ml_cmi

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Monitoring machine learning (ML)-based risk prediction algorithms in the presence of confounding medical interventions

https://github.com/joeloskarsson/graph-dgmrf

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Scalable Deep Gaussian Markov Random Fields for General Graphs

https://github.com/jonaselgammal/gpry

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Fast and robust Bayesian Inference using Gaussian Processes with GPry

https://github.com/josefina-catoni/ea-vae-uncertainty-in-latent-representations

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Uncertainty in latent representations of variational autoencoders optimized for visual tasks

https://github.com/josh3142/lowranklaplaceapproximation

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Optimal Subspace Inference for the Laplace Approximation of Bayesian Neural Networks

https://github.com/jplmlia/uq4k

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Uncertainty Quantification of the 4th kind; optimal posterior accuracy-uncertainty tradeoff with the minimum enclosing ball

https://github.com/jswu18/projected-langevin-sampling

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Near-Optimal Approximations for Bayesian Inference in Function Space

https://github.com/jtaghia/ciMRGP

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Conditionally Independent Multiresolution Gaussian Processes

https://github.com/juehang/ML-NSI

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Fast Bayesian Inference for Neutrino Non-Standard Interactions at Dark Matter Direct Detection Experiments

https://github.com/juice-jl/dice.jl

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Scaling Integer Arithmetic in Probabilistic Programs

https://github.com/julianmer/snfs-for-bayesian-inference-in-mrs

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Physics-Informed Sylvester Normalizing Flows for Bayesian Inference in Magnetic Resonance Spectroscopy

https://github.com/jxbz/implicit-bias

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Kernel Interpolation as a Bayes Point Machine

https://github.com/kaarthiksundar/bnn-opf

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Optimization Proxies using Limited Labeled Data and Training Time -- A Semi-Supervised Bayesian Neural Network Approach

https://github.com/karlnapf/kernel_hmc

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Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families

https://github.com/karrykkk/bayesdiff

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BayesDiff: Estimating Pixel-wise Uncertainty in Diffusion via Bayesian Inference

https://github.com/kclip/DSVGD

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Federated Generalized Bayesian Learning via Distributed Stein Variational Gradient Descent

https://github.com/kirk86/bdlood

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Ramifications of Approximate Posterior Inference for Bayesian Deep Learning in Adversarial and Out-of-Distribution Settings

https://github.com/kit-cel/ep-fft-bp

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Uncertainty Propagation in the Fast Fourier Transform

https://github.com/klemens-floege/svn_ensembles

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Stein Variational Newton Neural Network Ensembles

https://github.com/kmzzhang/nbi

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nbi: the Astronomer's Package for Neural Posterior Estimation

https://github.com/kolyanray/Bayesian-Causal-Inference

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Debiased Bayesian inference for average treatment effects

https://github.com/konpanousis/SB-LWTA

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Nonparametric Bayesian Deep Networks with Local Competition

https://github.com/konstantinhess/bayesian-neural-cde

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Bayesian Neural Controlled Differential Equations for Treatment Effect Estimation

https://github.com/konstantinos-p/bayesian-neural-networks-reading-list

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A Primer on Bayesian Neural Networks: Review and Debates

https://github.com/korobilis/hierarchicalbayes

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Bayesian Approaches to Shrinkage and Sparse Estimation

https://github.com/ksnxr/rlaf

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Riemannian Laplace Approximation with the Fisher Metric

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