SOTAVerified

Bayesian Inference

Bayesian Inference is a methodology that employs Bayes Rule to estimate parameters (and their full posterior).

Papers

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Showing 1941–1950 of 2226 papers

TitleStatusHype
Characteristics of Monte Carlo Dropout in Wide Neural Networks—0
Classified as unknown: A novel Bayesian neural network—0
Clustered Mallows Model—0
Clustering Time Series with Nonlinear Dynamics: A Bayesian Non-Parametric and Particle-Based Approach—0
Co-Creative Learning via Metropolis-Hastings Interaction between Humans and AI—0
Coherent Track-Before-Detect—0
Cohort effects in mortality modelling: a Bayesian state-space approach—0
Cold Posteriors through PAC-Bayes—0
Collapsed Variational Bayesian Inference for PCFGs—0
Bayesian Analysis of Combinatorial Gaussian Process Bandits—0
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Benchmark Results

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