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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 841–850 of 2226 papers

TitleStatusHype
Bayesian Active Learning for Scanning Probe Microscopy: from Gaussian Processes to Hypothesis Learning—0
Bayesian Low-Rank Interpolative Decomposition for Complex Datasets—0
Deterministic Langevin Monte Carlo with Normalizing Flows for Bayesian Inference—0
Consistent and fast inference in compartmental models of epidemics using Poisson Approximate LikelihoodsCode0
Optimal Neural Network Approximation of Wasserstein Gradient Direction via Convex OptimizationCode0
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
Marginal Post Processing of Bayesian Inference Products with Normalizing Flows and Kernel Density EstimatorsCode1
Emergent Communication through Metropolis-Hastings Naming Game with Deep Generative ModelsCode0
Extended molt phenology models improve inferences about molt duration and timingCode0
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Benchmark Results

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