Editore: LAP LAMBERT Academic Publishing, 2015
ISBN 10: 3659716286 ISBN 13: 9783659716287
Lingua: Inglese
Da: Lucky's Textbooks, Dallas, TX, U.S.A.
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Editore: LAP LAMBERT Academic Publishing, 2015
ISBN 10: 3659716286 ISBN 13: 9783659716287
Lingua: Inglese
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Editore: LAP LAMBERT Academic Publishing, 2015
ISBN 10: 3659716286 ISBN 13: 9783659716287
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Editore: LAP LAMBERT Academic Publishing 2015-06-03, 2015
ISBN 10: 3659716286 ISBN 13: 9783659716287
Lingua: Inglese
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Editore: LAP LAMBERT Academic Publishing, 2015
ISBN 10: 3659716286 ISBN 13: 9783659716287
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Editore: LAP LAMBERT Academic Publishing, 2015
ISBN 10: 3659716286 ISBN 13: 9783659716287
Lingua: Inglese
Da: preigu, Osnabrück, Germania
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Bayesian analysis: general framework | Includes multivariate regression analysis, model selection and response prediction | Joćo Casaca | Taschenbuch | 156 S. | Englisch | 2015 | LAP LAMBERT Academic Publishing | EAN 9783659716287 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
Editore: LAP LAMBERT Academic Publishing, 2015
ISBN 10: 3659716286 ISBN 13: 9783659716287
Lingua: Inglese
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Editore: LAP LAMBERT Academic Publishing, 2015
ISBN 10: 3659716286 ISBN 13: 9783659716287
Lingua: Inglese
Da: PBShop.store US, Wood Dale, IL, U.S.A.
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Editore: LAP LAMBERT Academic Publishing Jun 2015, 2015
ISBN 10: 3659716286 ISBN 13: 9783659716287
Lingua: Inglese
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The book 'Bayesian analysis: general framework' deals with Bayesian inference within the framework of four stochastic models: the normal random sample, the Gauss-Markov model, the beta-binomial model and the Poisson-gamma model. For each of these models, analytic expressions are presented for the posterior PDF, the prior predictive PDF and the posterior predictive PDF, under the Laplace prior PDF, the Jeffreys prior PDF and the conjugate prior PDF. Topics such as the elicitation of hyper-parameter for the conjugate prior PDF, the selection of models and prediction in multivariate regression analysis and the use of the Bayes factor in the test of hypothesis are dealt with more detail. 156 pp. Englisch.
Editore: LAP LAMBERT Academic Publishing Jun 2015, 2015
ISBN 10: 3659716286 ISBN 13: 9783659716287
Lingua: Inglese
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The book 'Bayesian analysis: general framework' deals with Bayesian inference within the framework of four stochastic models: the normal random sample, the Gauss-Markov model, the beta-binomial model and the Poisson-gamma model. For each of these models, analytic expressions are presented for the posterior PDF, the prior predictive PDF and the posterior predictive PDF, under the Laplace prior PDF, the Jeffreys prior PDF and the conjugate prior PDF. Topics such as the elicitation of hyper-parameter for the conjugate prior PDF, the selection of models and prediction in multivariate regression analysis and the use of the Bayes factor in the test of hypothesis are dealt with more detail.Books on Demand GmbH, Überseering 33, 22297 Hamburg 156 pp. Englisch.
Editore: LAP LAMBERT Academic Publishing, 2015
ISBN 10: 3659716286 ISBN 13: 9783659716287
Lingua: Inglese
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 61,90
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The book 'Bayesian analysis: general framework' deals with Bayesian inference within the framework of four stochastic models: the normal random sample, the Gauss-Markov model, the beta-binomial model and the Poisson-gamma model. For each of these models, analytic expressions are presented for the posterior PDF, the prior predictive PDF and the posterior predictive PDF, under the Laplace prior PDF, the Jeffreys prior PDF and the conjugate prior PDF. Topics such as the elicitation of hyper-parameter for the conjugate prior PDF, the selection of models and prediction in multivariate regression analysis and the use of the Bayes factor in the test of hypothesis are dealt with more detail.