SOTAVerified

Bayesian Inference

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

Papers

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Showing 491–500 of 2226 papers

TitleStatusHype
Worst-Case Analysis is Maximum-A-Posteriori Estimation—0
A Stochastic Particle Variational Bayesian Inference Inspired Deep-Unfolding Network for Non-Convex Parameter Estimation—0
A time-varying finance-led model for U.S. business cycles—0
Leveraging Self-Consistency for Data-Efficient Amortized Bayesian InferenceCode0
Amortizing intractable inference in large language modelsCode1
Fishnets: Information-Optimal, Scalable Aggregation for Sets and Graphs—0
Variational Inference for GARCH-family Models—0
Discriminative Training of VBx DiarizationCode1
Probabilistic Block Term Decomposition for the Modelling of Higher-Order Arrays—0
Towards a Unified Framework for Sequential Decision Making—0
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Benchmark Results

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