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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 221–230 of 2226 papers

TitleStatusHype
Noise-Aware Differentially Private Variational Inference—0
TRADE: Transfer of Distributions between External Conditions with Normalizing FlowsCode0
Conditional diffusions for amortized neural posterior estimationCode0
Scalable Random Feature Latent Variable ModelsCode1
A Bayesian Perspective on the Maximum Score Problem—0
Hyperboloid GPLVM for Discovering Continuous Hierarchies via Nonparametric Estimation—0
BI-EqNO: Generalized Approximate Bayesian Inference with an Equivariant Neural Operator Framework—0
A Trust-Region Method for Graphical Stein Variational Inference—0
Semiparametric Bayesian Inference for a Conditional Moment Equality Model—0
On Cold Posteriors of Probabilistic Neural Networks: Understanding the Cold Posterior Effect and A New Way to Learn Cold Posteriors with Tight Generalization GuaranteesCode0
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Benchmark Results

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