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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 2071–2080 of 2226 papers

TitleStatusHype
Marginal likelihood and model selection for Gaussian latent tree and forest models—0
Expectation propagation as a way of life: A framework for Bayesian inference on partitioned dataCode0
Hamming Ball Auxiliary Sampling for Factorial Hidden Markov Models—0
Bayesian Inference for Structured Spike and Slab Priors—0
A Bayesian model for identifying hierarchically organised states in neural population activity—0
Poisson Process Jumping between an Unknown Number of Rates: Application to Neural Spike Data—0
Scalable Inference for Neuronal Connectivity from Calcium Imaging—0
Nonparametric Bayesian inference on multivariate exponential families—0
Decoupled Variational Gaussian Inference—0
Neurons as Monte Carlo Samplers: Bayesian Inference and Learning in Spiking Networks—0
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Benchmark Results

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