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 1031–1040 of 2226 papers

TitleStatusHype
Hybridizing Physical and Data-driven Prediction Methods for Physicochemical Properties—0
Fast online inference for nonlinear contextual bandit based on Generative Adversarial Network—0
The efficacy and generalizability of conditional GANs for posterior inference in physics-based inverse problems—0
Sequential Bayesian experimental designs via reinforcement learning—0
Adjoint-aided inference of Gaussian process driven differential equations—0
Robust Bayesian Inference for Simulator-based Models via the MMD Posterior BootstrapCode0
Impact of Parameter Sparsity on Stochastic Gradient MCMC Methods for Bayesian Deep Learning—0
Black-box Bayesian inference for economic agent-based models—0
Exoplanet Characterization using Conditional Invertible Neural Networks—0
Optimal Regret Is Achievable with Bounded Approximate Inference Error: An Enhanced Bayesian Upper Confidence Bound FrameworkCode0
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Benchmark Results

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