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 2201–2226 of 2226 papers

TitleStatusHype
Kernel Bayes' Rule—0
Spike and Slab Variational Inference for Multi-Task and Multiple Kernel Learning—0
BAMBI: blind accelerated multimodal Bayesian inferenceCode0
Copula Processes—0
A rational decision making framework for inhibitory control—0
Tree-Structured Stick Breaking for Hierarchical Data—0
Global seismic monitoring as probabilistic inference—0
Neural Implementation of Hierarchical Bayesian Inference by Importance Sampling—0
Learning with Compressible Priors—0
Localizing Bugs in Program Executions with Graphical Models—0
Perceptual Multistability as Markov Chain Monte Carlo Inference—0
Large Scale Nonparametric Bayesian Inference: Data Parallelisation in the Indian Buffet Process—0
A Neural Implementation of the Kalman Filter—0
Non-Parametric Bayesian Dictionary Learning for Sparse Image Representations—0
A Bayesian Analysis of Dynamics in Free Recall—0
Analyzing human feature learning as nonparametric Bayesian inference—0
Stochastic Relational Models for Large-scale Dyadic Data using MCMC—0
Accelerating Bayesian Inference over Nonlinear Differential Equations with Gaussian Processes—0
Bayesian Experimental Design of Magnetic Resonance Imaging Sequences—0
Goal-directed decision making in prefrontal cortex: a computational framework—0
Hebbian Learning of Bayes Optimal Decisions—0
Bayesian Exponential Family PCA—0
MultiNest: an efficient and robust Bayesian inference tool for cosmology and particle physicsCode0
Fast Variational Inference for Large-scale Internet Diagnosis—0
Bayesian inference for the mixed conditional heteroskedasticity model—0
Evolutionary MCMC Sampling and Optimization in Discrete Spaces—0
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Benchmark Results

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