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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
Assessing the Safety and Reliability of Autonomous Vehicles from Road Testing—0
A statistical framework for GWAS of high dimensional phenotypes using summary statistics, with application to metabolite GWAS—0
A Stein Gradient Descent Approach for Doubly Intractable Distributions—0
A Stochastic Hybrid Framework for Driver Behavior Modeling Based on Hierarchical Dirichlet Process—0
A Stochastic Particle Variational Bayesian Inference Inspired Deep-Unfolding Network for Non-Convex Parameter Estimation—0
A Stochastic Robust Adaptive Systems Level Approach to Stabilizing Large-Scale Uncertain Markovian Jump Linear Systems—0
A stochastic version of Stein Variational Gradient Descent for efficient sampling—0
A Survey of Uncertainty Estimation in LLMs: Theory Meets Practice—0
A survey on Bayesian inference for Gaussian mixture model—0
A Survey on Blood Pressure Measurement Technologies: Addressing Potential Sources of Bias—0
A Symbolic and Statistical Learning Framework to Discover Bioprocessing Regulatory Mechanism: Cell Culture Example—0
Asymptotic Bayesian Generalization Error in Latent Dirichlet Allocation and Stochastic Matrix Factorization—0
Asymptotic properties of Bayesian inference in linear regression with a structural break—0
A Technical Critique of Some Parts of the Free Energy Principle—0
A theory of data variability in Neural Network Bayesian inference—0
A time-varying finance-led model for U.S. business cycles—0
A Tractable Fully Bayesian Method for the Stochastic Block Model—0
A transport approach to sequential simulation-based inference—0
A Trust-Region Method for Graphical Stein Variational Inference—0
Attention-Aware Answers of the Crowd—0
Attention-Driven Hierarchical Reinforcement Learning with Particle Filtering for Source Localization in Dynamic Fields—0
A Tutorial on Sparse Gaussian Processes and Variational Inference—0
Augmented Ensemble MCMC sampling in Factorial Hidden Markov Models—0
Augmented Message Passing Stein Variational Gradient Descent—0
A unified approach to mortality modelling using state-space framework: characterisation, identification, estimation and forecasting—0
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Benchmark Results

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