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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 991–1000 of 2226 papers

TitleStatusHype
Functional Distributional Semantics—0
Bayesian Learning for Neural Networks: an algorithmic survey—0
Bayesian Learning of Kernel Embeddings—0
Finite-Dimensional BFRY Priors and Variational Bayesian Inference for Power Law Models—0
Finite Horizon Throughput Maximization and Sensing Optimization in Wireless Powered Devices over Fading Channels—0
Finite Neural Networks as Mixtures of Gaussian Processes: From Provable Error Bounds to Prior Selection—0
Firefly Monte Carlo: Exact MCMC with Subsets of Data—0
Fishnets: Information-Optimal, Scalable Aggregation for Sets and Graphs—0
Bayesian Logistic Shape Model Inference: application to cochlea image segmentation—0
A theory of data variability in Neural Network Bayesian inference—0
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Benchmark Results

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