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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 1261–1270 of 2226 papers

TitleStatusHype
Sequential Likelihood-Free Inference with Neural ProposalCode0
Stochastic Frontier Analysis with Generalized Errors: inference, model comparison and averaging—0
Learning Not to Learn: Nature versus Nurture in Silico—0
Physics-constrained Bayesian inference of state functions in classical density-functional theory—0
Learning Manifold Implicitly via Explicit Heat-Kernel Learning—0
Bayesian Policy Search for Stochastic Domains—0
OutbreakFlow: Model-based Bayesian inference of disease outbreak dynamics with invertible neural networks and its application to the COVID-19 pandemics in GermanyCode1
TensorBNN: Bayesian Inference for Neural Networks using TensorflowCode1
Towards Scalable Bayesian Learning of Causal DAGs—0
Expressive yet Tractable Bayesian Deep Learning via Subnetwork Inference—0
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Benchmark Results

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