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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 61–70 of 2226 papers

TitleStatusHype
Successive Linear Approximation VBI for Joint Sparse Signal Recovery and Dynamic Grid Parameters EstimationCode1
Variational Inference with Gaussian Score MatchingCode1
Low-rank extended Kalman filtering for online learning of neural networks from streaming dataCode1
Parallelized Acquisition for Active Learning using Monte Carlo SamplingCode1
Conditional score-based diffusion models for Bayesian inference in infinite dimensionsCode1
Exact Bayesian Inference on Discrete Models via Probability Generating Functions: A Probabilistic Programming ApproachCode1
CosmoPower-JAX: high-dimensional Bayesian inference with differentiable cosmological emulatorsCode1
Plug-and-Play split Gibbs sampler: embedding deep generative priors in Bayesian inferenceCode1
Meta-Learned Models of CognitionCode1
PriorCVAE: scalable MCMC parameter inference with Bayesian deep generative modellingCode1
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Benchmark Results

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