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[null,"1.","Record Nr.",null,"UNINA9910139717303321"]
[null,null,"Autore",null,"Bolstad William M. <1943->"]
[null,null,"Titolo",null,"Understanding computational Bayesian statistics [[electronic resource] /] / William M. Bolstad"]
[null,null,"Pubbl/distr/stampa",null,"Hoboken, N.J., : Wiley, 2010"]
[null,null,"ISBN",null,"1-118-20992-3"]
[{"$nulls":4},"1-283-44603-0"]
[{"$nulls":4},"9786613446039"]
[{"$nulls":4},"0-470-56737-6"]
[{"$nulls":4},"0-470-56734-1"]
[null,null,"Descrizione fisica",null,"1 online resource (334 p.)"]
[null,null,"Collana",null,"Wiley series in computational statistics"]
[null,null,"Disciplina",null,"519.5/42"]
[{"$nulls":4},"519.542"]
[null,null,"Soggetti",null,"Bayesian statistical decision theory - Data processing"]
[{"$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","\"A John Wiley & Sons, Inc., publication.\""]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Introduction to Bayesian statistics -- Monte Carlo sampling from the posterior -- Bayesian inference -- Bayesian statistics using conjugate priors -- Markov chains -- Markov chain Monte Carlo sampling from the posterior -- Statistical inference from a Markov chain Monte Carlo sample -- Logistic regression -- Poisson regression and proportional hazards model -- Gibbs sampling and hierarchical models -- Going forward with Markov chain Monte Carlo -- Appendix A: Using the included Minitab macros -- Appendix B: Using the included R functions."]
[null,null,null,"Sommario/riassunto","A hands-on introduction to computational statistics from a Bayesian point of view Providing a solid grounding in statistics while uniquely covering the topics from a Bayesian perspective, Understanding Computational Bayesian Statistics successfully guides readers through this new, cutting-edge approach. With its hands-on treatment of the topic, the book shows how samples can be drawn from the posterior distribution when the formula giving its shape is all that is known, and how Bayesian inferences can be based on these samples from the posterior. These ideas are illustra"]