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Bayesian Inference

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

Papers

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Showing 1926–1950 of 2226 papers

TitleStatusHype
BAST: Bayesian Additive Regression Spanning Trees for Complex Constrained DomainCode0
Black-box density function estimation using recursive partitioningCode0
Fast and Robust Rank Aggregation against Model MisspecificationCode0
Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family ApproximationsCode0
Bayesian Inference with Certifiable Adversarial RobustnessCode0
Unified Probabilistic Deep Continual Learning through Generative Replay and Open Set RecognitionCode0
Reparameterization Gradients through Acceptance-Rejection Sampling AlgorithmsCode0
Black-box Coreset Variational InferenceCode0
Stochastic Approximation with Biased MCMC for Expectation MaximizationCode0
Stochastic Backpropagation and Approximate Inference in Deep Generative ModelsCode0
Model Reduction of Linear Dynamical Systems via Balancing for Bayesian InferenceCode0
Model selection and parameter inference in phylogenetics using Nested SamplingCode0
Faster MCMC for Gaussian Latent Position Network ModelsCode0
Variational Model Perturbation for Source-Free Domain AdaptationCode0
A Hierarchical Bayesian Model for Deep Few-Shot Meta LearningCode0
Fast Bayesian Inference for Neutrino Non-Standard Interactions at Dark Matter Direct Detection ExperimentsCode0
Monitoring machine learning (ML)-based risk prediction algorithms in the presence of confounding medical interventionsCode0
Bandit Learning with Implicit FeedbackCode0
Bayesian Inference with Anchored Ensembles of Neural Networks, and Application to Exploration in Reinforcement LearningCode0
Fast post-process Bayesian inference with Variational Sparse Bayesian QuadratureCode0
Specifying Weight Priors in Bayesian Deep Neural Networks with Empirical BayesCode0
Moreau-Yoshida Variational Transport: A General Framework For Solving Regularized Distributional Optimization ProblemsCode0
MPC-guided Imitation Learning of Neural Network Policies for the Artificial PancreasCode0
Fast yet Simple Natural-Gradient Descent for Variational Inference in Complex ModelsCode0
Reversible Jump Probabilistic ProgrammingCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1F-SWAAccuracy83.61—Unverified
2F-SWAGAccuracy80.93—Unverified