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 1626–1650 of 2226 papers

TitleStatusHype
Learning optimal Bayesian prior probabilities from data—0
Learning Others' Intentional Models in Multi-Agent Settings Using Interactive POMDPs—0
Learning Personalized Thermal Preferences via Bayesian Active Learning with Unimodality Constraints—0
Learning to Draw Samples with Amortized Stein Variational Gradient Descent—0
Learning with Compressible Priors—0
Learning without Recall by Random Walks on Directed Graphs—0
Learn to Estimate Labels Uncertainty for Quality Assurance—0
Leveraging Active Subspaces to Capture Epistemic Model Uncertainty in Deep Generative Models for Molecular Design—0
Lévy walks derived from a Bayesian decision-making model in non-stationary environments—0
Likelihood-Free Adaptive Bayesian Inference via Nonparametric Distribution Matching—0
Likelihood-free inference via classification—0
Linear, Machine Learning and Probabilistic Approaches for Time Series Analysis—0
Linear Noise Approximation Assisted Bayesian Inference on Mechanistic Model of Partially Observed Stochastic Reaction Network—0
Linguists Who Use Probabilistic Models Love Them: Quantification in Functional Distributional Semantics—0
Linking fast and slow: the case for generative models—0
LLMExplainer: Large Language Model based Bayesian Inference for Graph Explanation Generation—0
Local convexity of the TAP free energy and AMP convergence for Z2-synchronization—0
Localized Schrödinger Bridge Sampler—0
Localizing Bugs in Program Executions with Graphical Models—0
Locally Adaptive Bayesian Multivariate Time Series—0
Locally adaptive factor processes for multivariate time series—0
Locally Differentially Private Bayesian Inference—0
Location Tracking for Reconfigurable Intelligent Surfaces Aided Vehicle Platoons: Diverse Sparsities Inspired Approaches—0
Locking and Quacking: Stacking Bayesian model predictions by log-pooling and superposition—0
Looking at the posterior: accuracy and uncertainty of neural-network predictions—0
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Benchmark Results

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