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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 281–290 of 2226 papers

TitleStatusHype
Fully Bayesian Differential Gaussian Processes through Stochastic Differential Equations—0
Divide-and-Conquer Predictive Coding: a structured Bayesian inference algorithmCode1
Exchangeable Sequence Models Quantify Uncertainty Over Latent Concepts—0
Bayesian Kolmogorov Arnold Networks (Bayesian_KANs): A Probabilistic Approach to Enhance Accuracy and Interpretability—0
Meta-Posterior Consistency for the Bayesian Inference of Metastable System—0
Identification and Inference for Synthetic Control Methods with Spillover Effects: Estimating the Economic Cost of the Sudan Split—0
Persistent Sampling: Enhancing the Efficiency of Sequential Monte CarloCode1
Neural Surrogate HMC: Accelerated Hamiltonian Monte Carlo with a Neural Network Surrogate Likelihood—0
Variational Inference Using Material Point Method—0
Finite Neural Networks as Mixtures of Gaussian Processes: From Provable Error Bounds to Prior Selection—0
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Benchmark Results

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