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[null,"1.","Record Nr.",null,"UNINA9910145035003321"]
[null,null,"Autore",null,"Geweke John"]
[null,null,"Titolo",null,"Contemporary Bayesian econometrics and statistics [[electronic resource] /] / John Geweke"]
[null,null,"Pubbl/distr/stampa",null,"Hoboken, N.J., : John Wiley, c2005"]
[null,null,"ISBN",null,"1-280-27761-0"]
[{"$nulls":4},"9786610277612"]
[{"$nulls":4},"0-470-23694-9"]
[{"$nulls":4},"0-471-74473-5"]
[{"$nulls":4},"0-471-74472-7"]
[null,null,"Descrizione fisica",null,"1 online resource (322 p.)"]
[null,null,"Collana",null,"Wiley Series in Probability and Statistics ; ; v.537"]
[null,null,"Disciplina",null,"330.015195"]
[{"$nulls":4},"330.01519542"]
[{"$nulls":4},"330/.01/519542"]
[null,null,"Soggetti",null,"Econometrics"]
[{"$nulls":4},"Bayesian statistical decision theory"]
[{"$nulls":4},"Decision making - Mathematical models"]
[{"$nulls":4},"Electronic books."]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Contemporary Bayesian Econometrics and Statistics; Contents; Preface; 1. Introduction; 1.1 Two Examples; 1.1.1 Public School Class Sizes; 1.1.2 Value at Risk; 1.2 Observables, Unobservables, and Objects of Interest; 1.3 Conditioning and Updating; 1.4 Simulators; 1.5 Modeling; 1.6 Decisionmaking; 2. Elements of Bayesian Inference; 2.1 Basics; 2.2 Sufficiency, Ancillarity, and Nuisance Parameters; 2.2.1 Sufficiency; 2.2.2 Ancillarity; 2.2.3 Nuisance Parameters; 2.3 Conjugate Prior Distributions; 2.4 Bayesian Decision Theory and Point Estimation; 2.5 Credible Sets; 2.6 Model Comparison"]
[{"$nulls":4},"2.6.1 Marginal Likelihoods2.6.2 Predictive Densities; 3. Topics in Bayesian Inference; 3.1 Hierarchical Priors and Latent Variables; 3.2 Improper Prior Distributions; 3.3 Prior Robustness and the Density Ratio Class; 3.4 Asymptotic Analysis; 3.5 The Likelihood Principle; 4. Posterior Simulation; 4.1 Direct Sampling; 4.2 Acceptance and Importance Sampling; 4.2.1 Acceptance Sampling; 4.2.2 Importance Sampling; 4.3 Markov Chain Monte Carlo; 4.3.1 The Gibbs Sampler; "]
[{"$nulls":4},"4.3.2 The Metropolis-Hastings Algorithm; 4.4 Variance Reduction; 4.4.1 Concentrated Expectations; 4.4.2 Antithetic Sampling"]
[{"$nulls":4},"4.5 Some Continuous State Space Markov Chain Theory4.5.1 Convergence of the Gibbs Sampler; 4.5.2 Convergence of the Metropolis-Hastings Algorithm; 4.6 Hybrid Markov Chain Monte Carlo Methods; 4.6.1 Transition Mixtures; 4.6.2 Metropolis within Gibbs; 4.7 Numerical Accuracy and Convergence in Markov Chain Monte Carlo; 5. Linear Models; 5.1 BACC and the Normal Linear Regression Model; 5.2 Seemingly Unrelated Regressions Models; 5.3 Linear Constraints in the Linear Model; 5.3.1 Linear Inequality Constraints"]
[{"$nulls":4},"5.3.2 Conjectured Linear Restrictions, Linear Inequality Constraints, and Covariate Selection5.4 Nonlinear Regression; 5.4.1 Nonlinear Regression with Smoothness Priors; 5.4.2 Nonlinear Regression with Basis Functions; 6. Modeling with Latent Variables; 6.1 Censored Normal Linear Models; 6.2 Probit Linear Models; 6.3 The Independent Finite State Model; 6.4 Modeling with Mixtures of Normal Distributions; 6.4.1 The Independent Student-t Linear Model; 6.4.2 Normal Mixture Linear Models; 6.4.3 Generalizing the Observable Outcomes; 7. Modeling for Time Series"]
[{"$nulls":4},"7.1 Linear Models with Serial Correlation7.2 The First-Order Markov Finite State Model; 7.2.1 Inference in the Nonstationary Model; 7.2.2 Inference in the Stationary Model; 7.3 Markov Normal Mixture Linear Model; 8. Bayesian Investigation; 8.1 Implementing Simulation Methods; 8.1.1 Density Ratio Tests; 8.1.2 Joint Distribution Tests; 8.2 Formal Model Comparison; 8.2.1 Bayes Factors for Modeling with Common Likelihoods; 8.2.2 Marginal Likelihood Approximation Using Importance Sampling; 8.2.3 Marginal Likelihood Approximation Using Gibbs Sampling"]
[{"$nulls":4},"8.2.4 Density Ratio Marginal Likelihood Approximation"]
[null,null,null,"Sommario/riassunto","Tools to improve decision making in an imperfect worldThis publication provides readers with a thorough understanding of Bayesian analysis that is grounded in the theory of inference and optimal decision making. Contemporary Bayesian Econometrics and Statistics provides readers with state-of-the-art simulation methods and models that are used to solve complex real-world problems. Armed with a strong foundation in both theory and practical problem-solving tools, readers discover how to optimize decision making when faced with problems that involve limited or imperfect data.The b"]