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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 1051–1075 of 2226 papers

TitleStatusHype
A Latent Space Theory for Emergent Abilities in Large Language Models—0
Heuristic Search as Evidential Reasoning—0
Hamiltonian Adaptive Importance Sampling—0
A stochastic version of Stein Variational Gradient Descent for efficient sampling—0
Hamming Ball Auxiliary Sampling for Factorial Hidden Markov Models—0
Bayesian Reinforcement Learning: A Survey—0
A Kolmogorov-Smirnov test for the molecular clock on Bayesian ensembles of phylogenies—0
A Kernel Learning Method for Backward SDE Filter—0
Hebbian Learning of Bayes Optimal Decisions—0
A Stochastic Robust Adaptive Systems Level Approach to Stabilizing Large-Scale Uncertain Markovian Jump Linear Systems—0
Bayesian reconstruction of HIV transmission trees from viral sequences and uncertain infection times—0
A Closer Look at Disentangling in β-VAE—0
Bayesian Reasoning with Trained Neural Networks—0
A Stochastic Particle Variational Bayesian Inference Inspired Deep-Unfolding Network for Non-Convex Parameter Estimation—0
Meta-Posterior Consistency for the Bayesian Inference of Metastable System—0
Heterogeneous Learning from Demonstration—0
Hidden Markov Models Applied To Intraday Momentum Trading With Side Information—0
High-dimensional Bayesian inference via the Unadjusted Langevin Algorithm—0
Bayesian Probabilistic Matrix Factorization—0
Bayesian Predictive Coding—0
A Stochastic Hybrid Framework for Driver Behavior Modeling Based on Hierarchical Dirichlet Process—0
Bayesian Prediction-Powered Inference—0
A Stein Gradient Descent Approach for Doubly Intractable Distributions—0
AI-Powered Bayesian Inference—0
Graph Tracking in Dynamic Probabilistic Programs via Source Transformations—0
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Benchmark Results

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