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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 2181–2190 of 2226 papers

TitleStatusHype
Fully Bayesian inference for neural models with negative-binomial spiking—0
Homeostatic plasticity in Bayesian spiking networks as Expectation Maximization with posterior constraints—0
Burn-in, bias, and the rationality of anchoring—0
Fast Bayesian Inference for Non-Conjugate Gaussian Process Regression—0
Mixability in Statistical Learning—0
Improved Combinatory Categorial Grammar Induction with Boundary Words and Bayesian Inference—0
Laplace approximation for logistic Gaussian process density estimation and regressionCode0
Locally adaptive factor processes for multivariate time series—0
Bayesian Inference with Posterior Regularization and applications to Infinite Latent SVMs—0
Negative Binomial Process Count and Mixture ModelingCode0
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Benchmark Results

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