SOTAVerified

Bayesian Inference

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

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2071–2080 of 2226 papers

TitleStatusHype
Bayesian Inference for Structured Spike and Slab Priors—0
A Bayesian model for identifying hierarchically organised states in neural population activity—0
Neurons as Monte Carlo Samplers: Bayesian Inference and Learning in Spiking Networks—0
Nonparametric Bayesian inference on multivariate exponential families—0
Scalable Inference for Neuronal Connectivity from Calcium Imaging—0
Hamming Ball Auxiliary Sampling for Factorial Hidden Markov Models—0
Poisson Process Jumping between an Unknown Number of Rates: Application to Neural Spike Data—0
A Nonparametric Bayesian Approach to Uncovering Rat Hippocampal Population Codes During Spatial Navigation—0
Noise Benefits in Expectation-Maximization Algorithms—0
Big Learning with Bayesian Methods—0
Show:102550
← PrevPage 208 of 223Next →

Benchmark Results

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