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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 1031–1040 of 2226 papers

TitleStatusHype
A theory of representation learning gives a deep generalisation of kernel methods—0
A Mathematical Walkthrough and Discussion of the Free Energy Principle—0
Variational Inference with NoFAS: Normalizing Flow with Adaptive Surrogate for Computationally Expensive ModelsCode0
Modeling Item Response Theory with Stochastic Variational Inference—0
Uncertainty Quantification of the 4th kind; optimal posterior accuracy-uncertainty tradeoff with the minimum enclosing ballCode0
Modeling time evolving COVID-19 uncertainties with density dependent asymptomatic infections and social reinforcement—0
A survey on Bayesian inference for Gaussian mixture model—0
Approximate Bayesian Neural Doppler ImagingCode0
A fast asynchronous MCMC sampler for sparse Bayesian inferenceCode0
Stability and Convergence of Stochastic Particle Flow Filters—0
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Benchmark Results

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