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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 1191–1200 of 2226 papers

TitleStatusHype
On the relationship between a Gamma distributed precision parameter and the associated standard deviation in the context of Bayesian parameter inferenceCode0
Probabilistic Inference for Learning from Untrusted Sources—0
Full-Information Estimation of Heterogeneous Agent Models Using Macro and Micro DataCode1
Towards fast machine-learning-assisted Bayesian posterior inference of microseismic event location and source mechanismCode1
Scaling Up Bayesian Uncertainty Quantification for Inverse Problems using Deep Neural Networks—0
Maximum a Posteriori Inference of Random Dot Product Graphs via Conic Programming—0
Control-Data Separation and Logical Condition Propagation for Efficient Inference on Probabilistic Programs—0
Learning optimal Bayesian prior probabilities from data—0
Bayesian Context Aggregation for Neural Processes—0
Bayesian Neural Networks with Variance Propagation for Uncertainty Evaluation—0
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Benchmark Results

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