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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 1676–1700 of 2226 papers

TitleStatusHype
Mean and Variance Estimation Complexity in Arbitrary Distributions via Wasserstein Minimization—0
Mean Field for the Stochastic Blockmodel: Optimization Landscape and Convergence Issues—0
Mean Field Network based Graph Refinement with application to Airway Tree Extraction—0
Mean-field theory of graph neural networks in graph partitioning—0
Mean field variational Bayesian inference for support vector machine classification—0
Mean-field Variational Inference via Wasserstein Gradient Flow—0
Merging MCMC Subposteriors through Gaussian-Process Approximations—0
Message Passing Stein Variational Gradient Descent—0
Meta-Learning for Variational Inference—0
Meta-Learning Divergences of Variational Inference—0
Metropolis-CVAE: Bootstrapping Labels for Bayesian Inference via Semi-Supervised Conditional Variational Autoencoders—0
Metropolis-Hastings algorithm in joint-attention naming game: Experimental semiotics study—0
Metropolis-Hastings Captioning Game: Knowledge Fusion of Vision Language Models via Decentralized Bayesian Inference—0
Metropolis-Hastings view on variational inference and adversarial training—0
Metropolis Sampling—0
MFRL-BI: Design of a Model-free Reinforcement Learning Process Control Scheme by Using Bayesian Inference—0
Micro-CT Synthesis and Inner Ear Super Resolution via Generative Adversarial Networks and Bayesian Inference—0
Minimum Description Length Control—0
Mixability in Statistical Learning—0
Mixture Distributions for Scalable Bayesian Inference—0
Mixture modeling on related samples by ψ-stick breaking and kernel perturbation—0
Mixture of Bilateral-Projection Two-dimensional Probabilistic Principal Component Analysis—0
Model-based Bayesian inference of neural activity and connectivity from all-optical interrogation of a neural circuit—0
Model-based Bayesian Reinforcement Learning for Dialogue Management—0
Model-based Influence Diagrams for Machine Vision—0
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Benchmark Results

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