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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 1921–1930 of 2226 papers

TitleStatusHype
Accelerating Monte Carlo Bayesian Inference via Approximating Predictive Uncertainty over SimplexCode0
Fast, accurate and lightweight sequential simulation-based inference using Gaussian locally linear mappingsCode0
Fast and accurate approximate inference of transcript expression from RNA-seq dataCode0
Blang: Bayesian declarative modelling of general data structures and inference via algorithms based on distribution continuaCode0
Stein Variational Newton Neural Network EnsemblesCode0
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
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Benchmark Results

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