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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 1001–1025 of 2226 papers

TitleStatusHype
Do Bayesian Neural Networks Improve Weapon System Predictive Maintenance?—0
Bayesian Inference for Gaussian Process Classifiers with Annealing and Pseudo-Marginal MCMC—0
Flexible and Hierarchical Prior for Bayesian Nonnegative Matrix Factorization—0
Flexible Bayesian Inference on Partially Observed Epidemics—0
A Novel Resource Allocation for Anti-jamming in Cognitive-UAVs: an Active Inference Approach—0
Flexible Mixture Modeling on Constrained Spaces—0
Divide, Conquer, Combine Bayesian Decision Tree Sampling—0
Bayesian Inference for Gamma Models—0
Forecasting Macroeconomic Dynamics using a Calibrated Data-Driven Agent-based Model—0
Forecasting using incomplete models—0
Distribution Transformers: Fast Approximate Bayesian Inference With On-The-Fly Prior Adaptation—0
Distribution-Level AirComp for Wireless Federated Learning under Data Scarcity and Heterogeneity—0
Formalizing Neurath's Ship: Approximate Algorithms for Online Causal Learning—0
Formalizing Preference Utilitarianism in Physical World Models—0
Forming Real-World Human-Robot Cooperation for Tasks With General Goal—0
Bayesian inference for dynamic spatial quantile models with interactive effects—0
Foundation Models for Causal Inference via Prior-Data Fitted Networks—0
Free energy and inference in living systems—0
Free Energy Minimization: A Unified Framework for Modelling, Inference, Learning,and Optimization—0
Frequentist Guarantees of Distributed (Non)-Bayesian Inference—0
Semantic Information G Theory and Logical Bayesian Inference for Machine Learning—0
From Shannon's Channel to Semantic Channel via New Bayes' Formulas for Machine Learning—0
Distribution learning via neural differential equations: minimal energy regularization and approximation theory—0
Distributionally Robust Optimisation with Bayesian Ambiguity Sets—0
Bayesian inference for data-efficient, explainable, and safe robotic motion planning: A review—0
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Benchmark Results

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