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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 676–700 of 2226 papers

TitleStatusHype
Adams Conditioning and Likelihood Ratio Transfer Mediated Inference—0
A Markov Model of Machine Translation using Non-parametric Bayesian Inference—0
Deep importance sampling using tensor trains with application to a priori and a posteriori rare event estimation—0
Conditional simulation via entropic optimal transport: Toward non-parametric estimation of conditional Brenier maps—0
Confidence Calibration for Convolutional Neural Networks Using Structured Dropout—0
Confidence Estimation via Sequential Likelihood Mixing—0
Confidence in Large Language Model Evaluation: A Bayesian Approach to Limited-Sample Challenges—0
Confident in the Crowd: Bayesian Inference to Improve Data Labelling in Crowdsourcing—0
Conjugate Natural Selection—0
Connections between sequential Bayesian inference and evolutionary dynamics—0
Consciousness is entailed by compositional learning of new causal structures in deep predictive processing systems—0
Augmented Message Passing Stein Variational Gradient Descent—0
Big Learning with Bayesian Methods—0
Constrained Bayesian Inference for Low Rank Multitask Learning—0
Constrained Bayesian Networks: Theory, Optimization, and Applications—0
A majorization-minimization algorithm for nonnegative binary matrix factorization—0
Constrained Gaussian Process Motion Planning via Stein Variational Newton Inference—0
Constrained plasticity reserve as a natural way to control frequency and weights in spiking neural networks—0
Bayesian Bandit Algorithms with Approximate Inference in Stochastic Linear Bandits—0
Constraining subglacial processes from surface velocity observations using surrogate-based Bayesian inference—0
Consumer Demand Modeling During COVID-19 Pandemic—0
Contextual Similarity Distillation: Ensemble Uncertainties with a Single Model—0
BI-EqNO: Generalized Approximate Bayesian Inference with an Equivariant Neural Operator Framework—0
Bidirectional Recurrent Neural Networks as Generative Models—0
Augmented Ensemble MCMC sampling in Factorial Hidden Markov Models—0
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Benchmark Results

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