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 1026–1050 of 2226 papers

TitleStatusHype
Geometry of Score Based Generative Models—0
Global seismic monitoring as probabilistic inference—0
A Technical Critique of Some Parts of the Free Energy Principle—0
Bayes in the age of intelligent machines—0
A bi-partite generative model framework for analyzing and simulating large scale multiple discrete-continuous travel behaviour data—0
Bayesian Teaching Enables Probabilistic Reasoning in Large Language Models—0
Algorithms of the LDA model [REPORT]—0
Generative Model with Coordinate Metric Learning for Object Recognition Based on 3D Models—0
Bayesian taut splines for estimating the number of modes—0
Asymptotic properties of Bayesian inference in linear regression with a structural break—0
Algorithms for Inference in SVARs Identified with Sign and Zero Restrictions—0
Bayesian System Identification based on Hierarchical Sparse Bayesian Learning and Gibbs Sampling with Application to Structural Damage Assessment—0
Identification and Inference for Synthetic Control Methods with Spillover Effects: Estimating the Economic Cost of the Sudan Split—0
Asymptotic Bayesian Generalization Error in Latent Dirichlet Allocation and Stochastic Matrix Factorization—0
A Bayesian-Symbolic Approach to Learning and Reasoning for Intuitive Physics—0
Geometric Ergodicity in Modified Variations of Riemannian Manifold and Lagrangian Monte Carlo—0
Goal-Directed Behavior under Variational Predictive Coding: Dynamic Organization of Visual Attention and Working Memory—0
Bayesian Synthesis of Probabilistic Programs for Automatic Data Modeling—0
A Symbolic and Statistical Learning Framework to Discover Bioprocessing Regulatory Mechanism: Cell Culture Example—0
A Learning- and Scenario-based MPC Design for Nonlinear Systems in LPV Framework with Safety and Stability Guarantees—0
Bayesian stochastic blockmodeling—0
Bayesian State Estimation for Unobservable Distribution Systems via Deep Learning—0
A Survey on Blood Pressure Measurement Technologies: Addressing Potential Sources of Bias—0
A closer look at parameter identifiability, model selection and handling of censored data with Bayesian Inference in mathematical models of tumour growth—0
Bayesian shrinkage in mixture of experts models: Identifying robust determinants of class membership—0
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Benchmark Results

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