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 376–400 of 2226 papers

TitleStatusHype
Bayesian Experimental Design of Magnetic Resonance Imaging Sequences—0
Bayesian Exponential Family PCA—0
Bayesian Eye Tracking—0
Bayesian feature selection with strongly-regularizing priors maps to the Ising Model—0
Bayesian Deep Learning Via Expectation Maximization and Turbo Deep Approximate Message Passing—0
Bayesian Federated Model Compression for Communication and Computation Efficiency—0
Bayesian Causal Structural Learning with Zero-Inflated Poisson Bayesian Networks—0
Efficient MCMC Sampling with Dimension-Free Convergence Rate using ADMM-type Splitting—0
Bayesian Flow Networks in Continual Learning—0
Bayesian geoacoustic inversion using mixture density network—0
Bayesian Graph Convolutional Neural Networks Using Non-Parametric Graph Learning—0
Bayesian causal inference via probabilistic program synthesis—0
Analysing Quality of English-Hindi Machine Translation Engine Outputs Using Bayesian Classification—0
A New Parameterized Family of Stochastic Particle Flow Filters—0
A category theory framework for Bayesian learning—0
Bayesian Hypernetworks—0
Bayesian Image Quality Transfer with CNNs: Exploring Uncertainty in dMRI Super-Resolution—0
An Introduction to Animal Movement Modeling with Hidden Markov Models using Stan for Bayesian Inference—0
Bayesian imaging using Plug & Play priors: when Langevin meets Tweedie—0
Bayesian Imaging With Data-Driven Priors Encoded by Neural Networks: Theory, Methods, and Algorithms—0
Bayesian Incremental Inference Update by Re-using Calculations from Belief Space Planning: A New Paradigm—0
Bayesian Inference Accelerator for Spiking Neural Networks—0
Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC—0
Bayesian inference and neural estimation of acoustic wave propagation—0
Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization—0
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Benchmark Results

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