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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 826–850 of 2226 papers

TitleStatusHype
PAVI: Plate-Amortized Variational Inference—0
On the safe use of prior densities for Bayesian model selection—0
Scalable Deep Gaussian Markov Random Fields for General GraphsCode1
Fast Bayesian Inference with Batch Bayesian Quadrature via Kernel RecombinationCode1
A Learning- and Scenario-based MPC Design for Nonlinear Systems in LPV Framework with Safety and Stability Guarantees—0
BInGo: Bayesian Intrinsic Groupwise Registration via Explicit Hierarchical Disentanglement—0
Unifying Summary Statistic Selection for Approximate Bayesian ComputationCode0
Automated Circuit Sizing with Multi-objective Optimization based on Differential Evolution and Bayesian Inference—0
Information Threshold, Bayesian Inference and Decision-Making—0
Functional Ensemble DistillationCode0
Excess risk analysis for epistemic uncertainty with application to variational inference—0
Bayesian Inference of Stochastic Dynamical Networks—0
Bayesian Inference for the Multinomial Probit Model under Gaussian Prior Distribution—0
Variational inference via Wasserstein gradient flowsCode1
Robust Anytime Learning of Markov Decision ProcessesCode0
Bayesian Active Learning for Scanning Probe Microscopy: from Gaussian Processes to Hypothesis Learning—0
Bayesian Low-Rank Interpolative Decomposition for Complex Datasets—0
Deterministic Langevin Monte Carlo with Normalizing Flows for Bayesian Inference—0
Consistent and fast inference in compartmental models of epidemics using Poisson Approximate LikelihoodsCode0
Optimal Neural Network Approximation of Wasserstein Gradient Direction via Convex OptimizationCode0
Analytics of Business Time Series Using Machine Learning and Bayesian Inference—0
Non-Programmers Can Label Programs Indirectly via Active Examples: A Case Study with Text-to-SQLCode0
Marginal Post Processing of Bayesian Inference Products with Normalizing Flows and Kernel Density EstimatorsCode1
Emergent Communication through Metropolis-Hastings Naming Game with Deep Generative ModelsCode0
Extended molt phenology models improve inferences about molt duration and timingCode0
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Benchmark Results

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