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 541–550 of 2226 papers

TitleStatusHype
A Bounded p-norm Approximation of Max-Convolution for Sub-Quadratic Bayesian Inference on Additive Factors—0
Bob and Alice Go to a Bar: Reasoning About Future With Probabilistic Programs—0
Active pooling design in group testing based on Bayesian posterior prediction—0
Amortized Conditional Normalized Maximum Likelihood—0
BiSeg: Simultaneous Instance Segmentation and Semantic Segmentation with Fully Convolutional Networks—0
Barrier Certificates for Unknown Systems with Latent States and Polynomial Dynamics using Bayesian Inference—0
BARNN: A Bayesian Autoregressive and Recurrent Neural Network—0
Amortized Bayesian Workflow (Extended Abstract)—0
Black-Box Autoregressive Density Estimation for State-Space Models—0
BAR: Bayesian Activity Recognition using variational inference—0
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Benchmark Results

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