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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 1511–1520 of 2226 papers

TitleStatusHype
Stein Variational Gradient Descent for Approximate Bayesian Computation—0
Bijectors.jl: Flexible transformations for probability distributionsCode0
Bayesian Model Selection for Identifying Markov Equivalent Causal Graphs—0
Graph Tracking in Dynamic Probabilistic Programs via Source Transformations—0
Global Approximate Inference via Local Linearisation for Temporal Gaussian Processes—0
Exchangeable Variational Autoencoders with Applications to Genomic Data—0
Interpretable User Models via Decision-rule Gaussian Processes: Preliminary Results on Energy Storage—0
Gaussian Process Meta-Representations For Hierarchical Neural Network Weight Priors—0
Neural Permutation Processes—0
Challenges in Markov chain Monte Carlo for Bayesian neural networksCode0
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Benchmark Results

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