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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 1811–1820 of 2226 papers

TitleStatusHype
Dropout as a Structured Shrinkage PriorCode0
Bayesian Inference Gaussian Process Multiproxy Alignment of Continuous Signals (BIGMACS): Applications for PaleoceanographyCode0
Dynamical Hyperspectral Unmixing with Variational Recurrent Neural NetworksCode0
MACAW: A Causal Generative Model for Medical ImagingCode0
A Molecular Prior Distribution for Bayesian Inference Based on Wilson StatisticsCode0
Push: Concurrent Probabilistic Programming for Bayesian Deep LearningCode0
PDE-constrained Gaussian process surrogate modeling with uncertain data locationsCode0
Bayesian Approaches to Shrinkage and Sparse EstimationCode0
MAGIC: Modular Auto-encoder for Generalisable Model Inversion with Bias CorrectionsCode0
Compositional uncertainty in deep Gaussian processesCode0
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Benchmark Results

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