Bayesian statistical decision theory.
Overview
Works: | 226 works in 73 publications in 73 languages |
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Titles
Bayesian approach to global optimization : = theory and applications /
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Tools for statistical inference : = methods for the exploration of posterior distributions and likelihood functions /
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Bayesian inference and decision technuques : = bessays in honor of Bruno de Finetti /
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Subjective and objective Bayesian statistics : = principles, models, and applications /
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The Bayesian choice : = from decision-theoretic foundations to computational implementation /
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Applied Bayesian modeling and causal inference from incomplete-data perspectives : = an essential journey with Donald Rubin's statistical family /
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Theory of preliminary test and Stein-type estimation with applications /
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Interpreting probability = controversies and developments in the early twentieth century /
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Bayesian Networks and Influence Diagrams = A Guide to Construction and Analysis /
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Nonbayesian Decision Theory = Beliefs and Desires as Reasons for Action /
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Missing data in longitudinal studies : = strategies for Bayesian modeling and sensitivity analysis /
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Approximation methods for efficient learning of Bayesian networks /
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Probabilistic methods for bioinformatics = with an introduction to Bayesian networks /
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Bayesian economics through numerical methods = a guide to econometrics and decision-making with prior information /
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Bayesian ideas and data analysis : = an introduction for scientists and statisticians /
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Frontiers of statistical decision making and Bayesian analysis = in honor of James O. Berger /
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Bayesian missing data problems : = EM, data augmentation and noniterative computation /
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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 disease mapping = hierarchical modeling in spatial epidemiology /
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Bayesian smoothing and regression for longitudinal, spatial and event history data
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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
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Statistical rethinking : = a Bayesian course with examples in R and Stan /
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Bayesian inference for partially identified models : = exploring the limits of limited data /
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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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Introduction to Bayesian estimation and copula models of dependence /
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Maximizing information system availability through Bayesian belief network approaches = emerging research and opportunities /
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The impact of economic freedom on state legitimacy = an empirical investigation /
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Enhanced Bayesian network models for spatial time series prediction = recent research trend in data-driven predictive analytics /
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Likelihood and Bayesian inference = with applications in biology and medicine /
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Nonparametric Bayesian learning for collaborative robot multimodal introspection
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Bayesian data analysis for the behavioral and neural sciences : = non-calculus fundamentals /
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Variational methods for machine learning with applications to deep networks
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Case studies in applied Bayesian data science = CIRM Jean-Morlet Chair, Fall 2018 /
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Development of clinical decision support systems using Bayesian networks = with an example of a multi-disciplinary treatment decision for laryngeal cancer /
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The equation of knowledge : = from Bayes' rule to a unified philosophy of science /
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Test data engineering = latent rank analysis, biclustering, and Bayesian network /
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Bayesian tensor decomposition for signal processing and machine learning = modeling, tuning-free algorithms, and applications /
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Bayesian real-time system identification = from centralized to distributed approach /
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Statistical modeling using Bayesian latent Gaussian models = with applications in geophysics and environmental sciences /
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Bayesian analysis in statistics and econometrics : = essays in honor of Arnold Zellner /
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Statistical multiple integration : = proceedings of a joint summer research conference held at Humboldt University, June 17-23, 1989 /
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Temporal GIS : = advanced functions for field-based applications /
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Bayesian economics through numerical methods = a guide to econometrics and decision-making with prior information /
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Bayesian Nets and Causality = Philosophical and Computational Foundations
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Markov chain Monte Carlo : = stochastic simulation for Bayesian inference /
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Decision theory models for applications in artificial intelligence = concepts and solutions /
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Basic and advanced Bayesian structural equation modeling : = with applications in the medical and behavioral sciences /
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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 smoothing and regression for longitudinal, spatial and event history data /
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Bayesian phylogenetics : = methods, algorithms, and applications /
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Bayesian disease mapping : = hierarchical modeling in spatial epidemiology /
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Bayesian methods for the physical sciences = learning from examples in astronomy and physics /
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Bayesian data analysis in ecology using linear models withR, Bugs, and Stan
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Prior processes and their applications = nonparametric bayesian estimation /
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Bayesian risk management = a guide to model risk and sequential learning in financial markets /
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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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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 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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Genomics data analysis = false discovery rates and empirical Bayes methods /
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