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Winner of the 2016 De Groot Prize from the International Society for Bayesian Analysis
Now in its third edition, this classic book continues to take an applied approach to analysis using up-to-date Bayesian methods. Along with new and revised software code, this edition includes four new chapters on nonparametric modeling, updates the discussion of cross-validation and predictive information criteria, and improves convergence monitoring and effective sample size calculations for iterative simulation. It also covers weakly informative priors, boundary-avoiding priors, Hamiltonian Monte Carlo, variational Bayes, and expectation propagation. Data sets and other materials are available online.
Now in its third edition, this classic book continues to take an applied approach to analysis using up-to-date Bayesian methods. Along with new and revised software code, this edition includes four new chapters on nonparametric modeling, updates the discussion of cross-validation and predictive information criteria, and improves convergence monitoring and effective sample size calculations for iterative simulation. It also covers weakly informative priors, boundary-avoiding priors, Hamiltonian Monte Carlo, variational Bayes, and expectation propagation. Data sets and other materials are available online.
Winner of the 2016 De Groot Prize from the International Society for Bayesian Analysis
Now in its third edition, this classic book continues to take an applied approach to analysis using up-to-date Bayesian methods. Along with new and revised software code, this edition includes four new chapters on nonparametric modeling, updates the discussion of cross-validation and predictive information criteria, and improves convergence monitoring and effective sample size calculations for iterative simulation. It also covers weakly informative priors, boundary-avoiding priors, Hamiltonian Monte Carlo, variational Bayes, and expectation propagation. Data sets and other materials are available online.
Now in its third edition, this classic book continues to take an applied approach to analysis using up-to-date Bayesian methods. Along with new and revised software code, this edition includes four new chapters on nonparametric modeling, updates the discussion of cross-validation and predictive information criteria, and improves convergence monitoring and effective sample size calculations for iterative simulation. It also covers weakly informative priors, boundary-avoiding priors, Hamiltonian Monte Carlo, variational Bayes, and expectation propagation. Data sets and other materials are available online.
Über den Autor
Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Doanld B. Rubin
Inhaltsverzeichnis
Fundamentals of Bayesian Inference. Fundamentals of Bayesian Data Analysis. Advanced Computation. Regression Models. Nonlinear and Nonparametric Models. Appendices.
Details
Erscheinungsjahr: | 2013 |
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Fachbereich: | Wahrscheinlichkeitstheorie |
Genre: | Importe, Mathematik |
Rubrik: | Naturwissenschaften & Technik |
Medium: | Buch |
Inhalt: | Einband - fest (Hardcover) |
ISBN-13: | 9781439840955 |
ISBN-10: | 1439840954 |
Sprache: | Englisch |
Einband: | Gebunden |
Autor: |
Gelman, Andrew
Carlin, John B. Stern, Hal S. |
Auflage: | 3. Auflage |
Hersteller: | Chapman and Hall/CRC |
Verantwortliche Person für die EU: | Libri GmbH, Europaallee 1, D-36244 Bad Hersfeld, gpsr@libri.de |
Maße: | 260 x 183 x 41 mm |
Von/Mit: | Andrew Gelman (u. a.) |
Erscheinungsdatum: | 01.11.2013 |
Gewicht: | 1,457 kg |