Bayesian statistical decision theory.
概要
| 作品: | 118 作品在 60 項出版品 60 種語言 | |
|---|---|---|
書目資訊
Decision theory models for applications in artificial intelligence = concepts and solutions /
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Missing data in longitudinal studies = strategies for Bayesian modeling and sensitivity analysis /
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Bayesian missing data problems = EM, data augmentation and noniterative computation /
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Bayesian smoothing and regression for longitudinal, spatial and event history data
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Strategic economic decision-making = using bayesian belief networks to solve complex problems /
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A multiple-testing approach to the multivariate Behrens-Fisher problem = with simulations and examples in SAS /
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Bayesian signal processing : = classical, modern, and particle filtering methods /
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Tackling the inverse problem for non-autonomous systems = application to the life sciences /
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Regularization and Bayesian methods for inverse problems in signal and image processing DUP_1
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Regularization and Bayesian methods for inverse problems in signal and image processing
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Bayesian prediction and adaptive sampling algorithms for mobile sensor networks = online environmental field reconstruction in space and time /
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The manual of strategic economic decision making = using Bayesian belief networks to solve complex problems /
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Bayesian signal processing : = classical, modern, and particle filtering methods /
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Maximizing information system availability through Bayesian belief network approaches = emerging research and opportunities /
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Bayesian risk management = a guide to model risk and sequential learning in financial markets /
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Probabilistic finite element model updating using Bayesian statistics = applications to aeronautical and mechanical engineering /
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Let the evidence speak = using bayesian thinking in law, medicine, ecology and other areas /
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Bayesian claims reserving methods in non-life insurance with Stan = an introduction /
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Reasoning with probabilistic and deterministic graphical models : = exact algorithms /
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Bayesian networks for managing learner models in adaptive hypermedia systems = emerging research and opportunities /
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Applied hierarchical modeling in ecology. = analysis of distribution, abundance and species richness in R and BUGS /. Volume 1,. Prelude and static models
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Statistical modeling using Bayesian latent Gaussian models = with applications in geophysics and environmental sciences /
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