04194nam a22004455i 4500001001800000003000900018005001700027007001500044008004100059020001800100020001900118024003500137040000900172082001400181100003100195245014400226264004600370300003300416336002600449337002600475338003900501347002400540490004500564505034500609520227100954650001603225650002903241650001603270650003603286700003103322700002903353710003403382773002003416776003603436830004503472856010303517942001203620999001703632952009903649978-0-387-35433-0DE-He21320260521091910.0cr nn 008mamaa100301s2006    xxu|    s    |||| 0|eng d  a9780387354330  a997803873543307 a10.1007/978-0-387-35433-02doi  cCICY04a519.52231 aGhosh, Jayanta K.eauthor.13aAn Introduction to Bayesian Analysish[recurso electrónico] :bTheory and Methods /cby Jayanta K. Ghosh, Mohan Delampady, Tapas Samanta. 1aNew York, NY :bSpringer New York,c2006.  aXIV, 352p.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  arecurso en líneabcr2rdacarrier  atext filebPDF2rda1 aSpringer Texts in Statistics,x1431-875X0 aStatistical Preliminaries -- Bayesian Inference and Decision Theory -- Utility, Prior, and Bayesian Robustness -- Large Sample Methods -- Choice of Priors for Low-dimensional Parameters -- Hypothesis Testing and Model Selection -- Bayesian Computations -- Some Common Problems in Inference -- High-dimensional Problems -- Some Applications.  aThis is a graduate-level textbook on Bayesian analysis blending modern Bayesian theory, methods, and applications. Starting from basic statistics, undergraduate calculus and linear algebra, ideas of both subjective and objective Bayesian analysis are developed to a level where real-life data can be analyzed using the current techniques of statistical computing. Advances in both low-dimensional and high-dimensional problems are covered, as well as important topics such as empirical Bayes and hierarchical Bayes methods and Markov chain Monte Carlo (MCMC) techniques. Many topics are at the cutting edge of statistical research. Solutions to common inference problems appear throughout the text along with discussion of what prior to choose. There is a discussion of elicitation of a subjective prior as well as the motivation, applicability, and limitations of objective priors. By way of important applications the book presents microarrays, nonparametric regression via wavelets as well as DMA mixtures of normals, and spatial analysis with illustrations using simulated and real data. Theoretical topics at the cutting edge include high-dimensional model selection and Intrinsic Bayes Factors, which the authors have successfully applied to geological mapping. The style is informal but clear. Asymptotics is used to supplement simulation or understand some aspects of the posterior. J.K. Ghosh has been Director and Jawaharlal Nehru Professor at the Indian Statistical Institute and President of the International Statistical Institute. He is currently a professor of statistics at Purdue University and professor emeritus at the Indian Statistical Institute. He has been the editor of Sankhya and has served on the editorial boards of several journals including the Annals of Statistics. His current interests in Bayesian analysis include asymptotics, nonparametric methods, high-dimensional model selection, reliability and survival analysis, bioinformatics, astrostatistics and sparse and not so sparse mixtures. Mohan Delampady and Tapas Samanta are both professors of statistics at the Indian Statistical Institute and both are interested in Bayesian inference, specifically in topics such as model selection, asymptotics, robustness and nonparametrics. 0aSTATISTICS. 0aMATHEMATICAL STATISTICS.14aSTATISTICS.24aSTATISTICAL THEORY AND METHODS.1 aDelampady, Mohan.eauthor.1 aSamanta, Tapas.eauthor.2 aSpringerLink (Online service)0 tSpringer eBooks08iPrinted edition:z9780387400846 0aSpringer Texts in Statistics,x1431-875X40uhttp://dx.doi.org/10.1007/978-0-387-35433-0zVer el texto completo en las instalaciones del CICY  2ddccER  c33390d33390  00102ddc40708LEaCICYbCICYcELd2025-07-10l0o519.5r2025-07-10 08:39:55w2025-07-10yER