Artboard 33 Artboard 16 Artboard 18 Artboard 15 Artboard 21 Artboard 1 Artboard 2 Artboard 5 Artboard 45 Artboard 45 Artboard 22 Artboard 9 Artboard 23 Artboard 17? Artboard 28 Artboard 43 Artboard 49 Artboard 47 Artboard 38 Artboard 32 Artboard 8 Artboard 22 Artboard 5 Artboard 25 Artboard 1 Artboard 42 Artboard 11 Artboard 41 Artboard 13 Artboard 23 Artboard 10 Artboard 4 Artboard 9 Artboard 20 Artboard 6 Artboard 11 Artboard 7 Artboard 3 Artboard 3 Artboard 12 Artboard 25 Artboard 34 Artboard 39 Artboard 24 Artboard 13 Artboard 19 Artboard 7 Artboard 24 Artboard 31 Artboard 4 Artboard 14 Artboard 27 Artboard 30 Artboard 36 Artboard 44 Artboard 12 Artboard 17 Artboard 17 Artboard 6 Artboard 27 Artboard 19 Artboard 30 Artboard 29 Artboard 29 Artboard 26 Artboard 18 Artboard 2 Artboard 20 Artboard 35 Artboard 15 Artboard 14 Artboard 48 Artboard 50 Artboard 26 Artboard 16 Artboard 40 Artboard 21 Artboard 29 Artboard 10 Artboard 37 Artboard 3 Artboard 3 Artboard 46 Artboard 8

Introduction To Probability Simulation And Gibbs Sampling With R (Cód: 2028418)

Eric A. Suess; Bruce E. Trumbo

SPRINGER VERLAG POD

Ooopss! Este produto está temporariamente indisponível.
Mas não se preocupe, nós avisamos quando ele chegar.

Ooops! Este produto não está mais a venda.
Mas não se preocupe, temos uma versão atualizada para você.

Ooopss! Este produto está fora de linha, mas temos outras opções para você.
Veja nossas sugestões abaixo!

Economize até R$ 0,00

R$ 317,99 (-15%) no boleto
R$ 374,10 em até 10x de R$ 37,41 sem juros
Cartão Saraiva R$ 355,40 (-5%) em até 1x no cartão ou em até 15x de R$ 24,94 sem juros

Crédito:
Boleto:
Cartão Saraiva:

Total: R$0,00

Em até 10x sem juros de R$ 0,00


Introduction To Probability Simulation And Gibbs Sampling With R

R$374,10

Descrição

The first seven chapters use R for probability simulation and computation, including random number generation, numerical and Monte Carlo integration, and finding limiting distributions of Markov Chains with both
discrete and continuous states. Applications include coverage probabilities of binomial confidence intervals, estimation of disease prevalence from screening tests, parallel redundancy for improved reliability of systems, and
various kinds of genetic modeling. These initial chapters can be used for a non-Bayesian course in the simulation of applied probability models and Markov Chains. Chapters 8 through 10 give a brief introduction to Bayesian
estimation and illustrate the use of Gibbs samplers to find posterior distributions and interval estimates, including some examples in which traditional methods do not give satisfactory results. WinBUGS software is introduced
with a detailed explanation of its interface and examples of its use for Gibbs sampling for Bayesian estimation. No previous experience using R is required. An appendix introduces R, and complete R code is included
for almost all computational examples and problems (along with comments and explanations). Noteworthy features of the book are its intuitive approach, presenting ideas with examples from biostatistics, reliability, and other
fields; its large number of figures; and its extraordinarily large number of problems (about a third of the pages), ranging from simple drill to presentation of additional topics. Hints and answers are provided for many of the
problems. These features make the book ideal for students of statistics at the senior undergraduate and at the beginning graduate levels.

Características

Peso 0.45 Kg
Produto sob encomenda Sim
Marca SPRINGER VERLAG POD
I.S.B.N. 9780387402734
Referência 9780387402734
Altura 23.40 cm
Largura 156.00 cm
Profundidade 1.72 cm
Número de Páginas 324
Idioma Inglês
Cód. Barras 9780387402734
Ano da edição 2010
AutorEric A. Suess; Bruce E. Trumbo