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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
Scalable Metropolis-Hastings for Exact Bayesian Inference with Large DatasetsCode0
What does the free energy principle tell us about the brain?—0
Fitting A Mixture Distribution to Data: TutorialCode0
Theory of Minds: Understanding Behavior in Groups Through Inverse Planning—0
A bi-partite generative model framework for analyzing and simulating large scale multiple discrete-continuous travel behaviour data—0
Mixed Variational InferenceCode0
Multi-modal Ensemble Classification for Generalized Zero Shot Learning—0
Bayesian shrinkage in mixture of experts models: Identifying robust determinants of class membership—0
Undirected Graphical Models as Approximate PosteriorsCode0
A Comprehensive guide to Bayesian Convolutional Neural Network with Variational InferenceCode0
Uncertainty-Based Out-of-Distribution Detection in Deep Reinforcement Learning—0
Can You Trust This Prediction? Auditing Pointwise Reliability After Learning—0
Guess Who's Coming (and Who's Going): Bringing Perspective to the Rational Speech Acts Framework—0
Accelerated MM Algorithms for Ranking Scores Inference from Comparison DataCode0
Probabilistic Streaming Tensor DecompositionCode0
Meta Architecture SearchCode0
Surrogate-assisted Bayesian inversion for landscape and basin evolution modelsCode0
Probabilistic Model Checking of Robots Deployed in Extreme Environments—0
Physics-Based Learning for Robotic Environmental Sensing—0
Sampling-based Bayesian Inference with gradient uncertainty—0
Efficient transfer learning and online adaptation with latent variable models for continuous control—0
That's Mine! Learning Ownership Relations and Norms for Robots—0
Predictive Approximate Bayesian Computation via Saddle Points—0
Mean Field for the Stochastic Blockmodel: Optimization Landscape and Convergence Issues—0
Learning Others' Intentional Models in Multi-Agent Settings Using Interactive POMDPs—0
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Benchmark Results

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