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 731–740 of 2226 papers

TitleStatusHype
Entropy-based Training Methods for Scalable Neural Implicit Sampler—0
In-Context Learning through the Bayesian PrismCode0
Latent Optimal Paths by Gumbel Propagation for Variational Bayesian Dynamic ProgrammingCode0
Interval Load Forecasting for Individual Households in the Presence of Electric Vehicle Charging—0
ReLU to the Rescue: Improve Your On-Policy Actor-Critic with Positive AdvantagesCode0
Robust Bayesian Inference for Berkson and Classical Measurement Error Models—0
On Mixing Rates for Bayesian CART—0
Metropolis-Hastings algorithm in joint-attention naming game: Experimental semiotics study—0
Deep learning and MCMC with aggVAE for shifting administrative boundaries: mapping malaria prevalence in KenyaCode0
Recursive Metropolis-Hastings Naming Game: Symbol Emergence in a Multi-agent System based on Probabilistic Generative Models—0
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Benchmark Results

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