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 271–280 of 2226 papers

TitleStatusHype
Correntropy-Based Improper Likelihood Model for Robust Electrophysiological Source Imaging—0
DynamicRouteGPT: A Real-Time Multi-Vehicle Dynamic Navigation Framework Based on Large Language Models—0
Loss-based Bayesian Sequential Prediction of Value at Risk with a Long-Memory and Non-linear Realized Volatility Model—0
General Intelligent Imaging and Uncertainty Quantification by Deterministic Diffusion Model—0
Amortized Bayesian Multilevel Models—0
Fast Burst-Sparsity Learning Approach for Massive MIMO-OTFS Channel Estimation—0
Enhanced BPINN Training Convergence in Solving General and Multi-scale Elliptic PDEs with Noise—0
InVAErt networks for amortized inference and identifiability analysis of lumped parameter hemodynamic modelsCode0
Value of Information and Reward Specification in Active Inference and POMDPs—0
Bayesian inference to improve quality of Retrieval Augmented Generation—0
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Benchmark Results

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