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 826–850 of 2226 papers

TitleStatusHype
Bayesian Inference of Natural Rankings in Incomplete Competition Networks—0
Approximate Inference for Spectral Mixture Kernel—0
Bayesian inference of mean velocity fields and turbulence models from flow MRI—0
Approximate Gibbs Sampler for Efficient Inference of Hierarchical Bayesian Models for Grouped Count Data—0
Bayesian Inference of Log Determinants—0
Approximate Decentralized Bayesian Inference—0
Advancing Autonomous Vehicle Safety: A Combined Fault Tree Analysis and Bayesian Network Approach—0
Accelerating Monte Carlo Bayesian Inference via Approximating Predictive Uncertainty over the Simplex—0
A Bayesian model for identifying hierarchically organised states in neural population activity—0
A Bayesian Analysis of Dynamics in Free Recall—0
Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization—0
Bayesian inference of composition-dependent phase diagrams—0
Bayesian Inference of Bijective Non-Rigid Shape Correspondence—0
Approximate Bayesian inference in spatial environments—0
Advancing Audio Emotion and Intent Recognition with Large Pre-Trained Models and Bayesian Inference—0
Bayesian inference of a new Mallows model for characterising symptom sequences applied in primary progressive aphasia—0
Approximate Bayesian Inference in Linear State Space Models for Intermittent Demand Forecasting at Scale—0
Bayesian Inference in Sparse Gaussian Graphical Models—0
Bayesian Inference in Recurrent Explicit Duration Switching Linear Dynamical Systems—0
Approximate Bayesian inference from noisy likelihoods with Gaussian process emulated MCMC—0
Accelerating MCMC via Parallel Predictive Prefetching—0
Bayesian Inference in Physics-Driven Problems with Adversarial Priors—0
Bayesian Inference in Physics-Based Nonlinear Flame Models—0
Bayesian Inference in Model-Based Machine Vision—0
Bayesian Inference in High-Dimensional Time-Serieswith the Orthogonal Stochastic Linear Mixing Model—0
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Benchmark Results

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