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 1176–1200 of 2226 papers

TitleStatusHype
Neural Posterior Regularization for Likelihood-Free InferenceCode0
ScrofaZero: Mastering Trick-taking Poker Game Gongzhu by Deep Reinforcement LearningCode0
Scalable Bayesian Inverse Reinforcement LearningCode1
Projected Wasserstein gradient descent for high-dimensional Bayesian inferenceCode0
Bayesian Inference with Certifiable Adversarial RobustnessCode0
Variational Inference for Deblending Crowded StarfieldsCode1
Bayesian multiscale deep generative model for the solution of high-dimensional inverse problemsCode0
A Bayesian approach for extracting free energy profiles from cryo-electron microscopy experiments using a path collective variableCode1
Bayesian data-driven discovery of partial differential equations with variable coefficients—0
Bayesian Neural Networks for Virtual Flow Metering: An Empirical StudyCode0
Modeling German Word Order Acquisition via Bayesian Inference—0
Human Inference in Changing Environments With Temporal Structure—0
Fundamental limits and algorithms for sparse linear regression with sublinear sparsity—0
Bayesian hierarchical stacking: Some models are (somewhere) usefulCode1
Bayesian Inference ForgettingCode0
On the relationship between a Gamma distributed precision parameter and the associated standard deviation in the context of Bayesian parameter inferenceCode0
Probabilistic Inference for Learning from Untrusted Sources—0
Full-Information Estimation of Heterogeneous Agent Models Using Macro and Micro DataCode1
Towards fast machine-learning-assisted Bayesian posterior inference of microseismic event location and source mechanismCode1
Scaling Up Bayesian Uncertainty Quantification for Inverse Problems using Deep Neural Networks—0
Maximum a Posteriori Inference of Random Dot Product Graphs via Conic Programming—0
Control-Data Separation and Logical Condition Propagation for Efficient Inference on Probabilistic Programs—0
Learning optimal Bayesian prior probabilities from data—0
Bayesian Context Aggregation for Neural Processes—0
Bayesian Neural Networks with Variance Propagation for Uncertainty Evaluation—0
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Benchmark Results

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