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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 1961–1970 of 2226 papers

TitleStatusHype
Pseudo-Marginal Hamiltonian Monte Carlo—0
Kernel Bayesian Inference with Posterior Regularization—0
Functional Distributional Semantics—0
Efficient Attack Graph Analysis through Approximate Inference—0
Bayesian Inference on Matrix Manifolds for Linear Dimensionality Reduction—0
A unified approach to mortality modelling using state-space framework: characterisation, identification, estimation and forecasting—0
Variational Bayesian Inference for Hidden Markov Models With Multivariate Gaussian Output Distributions—0
PAC-Bayesian Theory Meets Bayesian Inference—0
Merging MCMC Subposteriors through Gaussian-Process Approximations—0
Communication-Efficient Distributed Statistical Inference—0
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Benchmark Results

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