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 1201–1210 of 2226 papers

TitleStatusHype
Scalable Bayesian inference of dendritic voltage via spatiotemporal recurrent state space models—0
Scalable Bayesian Inverse Reinforcement Learning by Auto-Encoding Reward—0
Scalable Bayesian Learning for State Space Models using Variational Inference with SMC Samplers—0
Scalable Control Variates for Monte Carlo Methods via Stochastic Optimization—0
Scalable Decipherment for Machine Translation via Hash Sampling—0
Scalable Discrete Sampling as a Multi-Armed Bandit Problem—0
Scalable Gaussian Processes with Grid-Structured Eigenfunctions (GP-GRIEF)—0
Scalable Inference for Neuronal Connectivity from Calcium Imaging—0
Scalable Inference for Nonparametric Hawkes Process Using Pólya-Gamma Augmentation—0
Scalable Nonparametric Bayesian Inference on Point Processes with Gaussian Processes—0
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Benchmark Results

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