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 1091–1100 of 2226 papers

TitleStatusHype
Compositional Modeling of Nonlinear Dynamical Systems with ODE-based Random FeaturesCode0
RNN with Particle Flow for Probabilistic Spatio-temporal ForecastingCode1
Bayesian Boosting for Linear Mixed Models—0
Nonlinear Hawkes Processes in Time-Varying System—0
Scalable conditional deep inverse Rosenblatt transports using tensor-trains and gradient-based dimension reductionCode1
Bridging physiological and perceptual views of autism by means of sampling-based Bayesian inference—0
Antipodes of Label Differential Privacy: PATE and ALIBICode1
Knowing when we do not know: Bayesian continual learning for sensing-based analysis tasks—0
Context-tree weighting for real-valued time series: Bayesian inference with hierarchical mixture models—0
Canonical Cortical Circuits and the Duality of Bayesian Inference and Optimal Control—0
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Benchmark Results

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