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 961–970 of 2226 papers

TitleStatusHype
Bayesian Inference of Stochastic Dynamical Networks—0
Bayesian Inference for the Multinomial Probit Model under Gaussian Prior Distribution—0
Robust Anytime Learning of Markov Decision ProcessesCode0
Bayesian Low-Rank Interpolative Decomposition for Complex Datasets—0
Bayesian Active Learning for Scanning Probe Microscopy: from Gaussian Processes to Hypothesis Learning—0
Deterministic Langevin Monte Carlo with Normalizing Flows for Bayesian Inference—0
Optimal Neural Network Approximation of Wasserstein Gradient Direction via Convex OptimizationCode0
Consistent and fast inference in compartmental models of epidemics using Poisson Approximate LikelihoodsCode0
Analytics of Business Time Series Using Machine Learning and Bayesian Inference—0
Non-Programmers Can Label Programs Indirectly via Active Examples: A Case Study with Text-to-SQLCode0
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Benchmark Results

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