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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 301–325 of 2226 papers

TitleStatusHype
Off-grid Channel Estimation for Orthogonal Delay-Doppler Division Multiplexing Using Grid Refinement and Adjustment—0
Predictive Coding Networks and Inference Learning: Tutorial and Survey—0
Electrostatics-based particle sampling and approximate inferenceCode0
Joint Channel and Data Estimation for Multiuser Extremely Large-Scale MIMO Systems—0
Torchtree: flexible phylogenetic model development and inference using PyTorchCode1
Efficient, Multimodal, and Derivative-Free Bayesian Inference With Fisher-Rao Gradient FlowsCode2
Hierarchical thematic classification of major conference proceedings—0
Bayesian Bandit Algorithms with Approximate Inference in Stochastic Linear Bandits—0
Bayesian Inference for Multidimensional Welfare Comparisons—0
Conditional score-based diffusion models for solving inverse problems in mechanics—0
Integrating time-resolved nrf2 gene-expression data into a full GUTS model as a proxy for toxicodynamic damage in zebrafish embryo—0
DistPred: A Distribution-Free Probabilistic Inference Method for Regression and ForecastingCode2
Electricity Spot Prices Forecasting Using Stochastic Volatility ModelsCode0
Domain Agnostic Conditional Invariant Predictions for Domain Generalization—0
Verbalized Probabilistic Graphical Modeling with Large Language Models—0
Stochastic full waveform inversion with deep generative prior for uncertainty quantification—0
Theoretical Guarantees for Variational Inference with Fixed-Variance Mixture of Gaussians—0
Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks: An Extended Investigation—0
Reparameterization invariance in approximate Bayesian inference—0
Development of Bayesian Component Failure Models in E1 HEMP Grid Analysis—0
Event-horizon-scale Imaging of M87* under Different Assumptions via Deep Generative Image Priors—0
Logistic Variational Bayes RevisitedCode0
Is In-Context Learning in Large Language Models Bayesian? A Martingale PerspectiveCode0
Information limits and Thouless-Anderson-Palmer equations for spiked matrix models with structured noiseCode0
Bayesian Online Natural Gradient (BONG)Code0
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Benchmark Results

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