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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 1281–1290 of 2226 papers

TitleStatusHype
Deep reinforcement learning driven inspection and maintenance planning under incomplete information and constraints—0
DeepRV: pre-trained spatial priors for accelerated disease mapping—0
Deep Stable neural networks: large-width asymptotics and convergence rates—0
Demystifying excessively volatile human learning: A Bayesian persistent prior and a neural approximation—0
Density Estimation via Bayesian Inference Engines—0
Dependent Multinomial Models Made Easy: Stick-Breaking with the Polya-gamma Augmentation—0
De-randomizing MCMC dynamics with the diffusion Stein operator—0
Designing Perceptual Puzzles by Differentiating Probabilistic Programs—0
Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks: An Extended Investigation—0
Deterministic and statistical calibration of constitutive models from full-field data with parametric physics-informed neural networks—0
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Benchmark Results

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