CRAI - Universidad del Rosario Koha › Detalles de: Bayesian statistics

Bayesian statistics

por Lee, Peter M.

Declaración de edición:Cuarta edición Publicado por : Wiley (Cambridge (Reino Unido)) Detalles físicos: xxiii, 462 páginas gráficas ISBN:1118332571; 9781118332573 (4a.Ed.).

Tema(s): Teoría bayesiana de decisiones estadísticas | Probabilidades | Estadística | Matemáticas

Año : 2012

Existencias ( 1 )
Notas de título ( 4 )

Tipo de ítem	Ubicación actual	Colección	Signatura	Info Vol	Copia número	Estado	Fecha de vencimiento	Código de barras	Reserva de ítems
Libro	Quinta de Mutis Mezanecs	Libro	519.542 L477ba (Navegar estantería)	4a.Ed.	Ej.1	Disponible		200029520

Total de reservas: 0

Incluye referencias bibliográficas e índices

Preliminaries. Probability and Bayes' theorem. -- Examples on Bayes' theorem. -- Random variables. -- Several random variables. -- Means and variances. -- Bayesian inference for the normal distribution. Nature of Bayesian inference. -- Normal prior and likelihood. -- Several normal observations with a normal prior. -- Dominant likelihoods. -- Locally uniform priors. -- Highest density regions. -- Normal variance --HDRs for the normal variance. -- The role of sufficiency. -- Conjugate prior distributions. -- The exponential family. -- Normal mean and variance both unknown. -- Conjugate joint prior for the normal distribution. -- Some other common distributions. The binomial distribution. -- Reference prior for the binomial likelihood. -- Jeffreys' rule. -- The Poisson distribution. -- The uniform distribution. -- Reference prior for the uniform distribution. -- The tramcar problem. -- The first digit problem: invariant priors. -- The circular normal distribution. -- Approximations based on the likelihood. -- Reference posterior distributions. -- Hypothesis testing. Hypothesis testing. -- One-sided hypothesis tests. -- Lindley's method. -- Point (or sharp) null hypotheses with prior information. -- Point null hypotheses for the normal distribution. -- The Doogian philosophy. -- Two-sample problems. Two-sample problems- both variances unknown. -- Variances unknown but equal. -- Variances unknown and unequal (Behrens-Fisher problem). -- The Behrens-Fisher controversy. -- Inferences concerning a variance ratio. -- Comparison of two proportions: the 2X2 table. -- Correlation, regression and the analysis of variance. Theory of the correlation coefficient. -- Examples on the use of the correlation coefficient. -- Regression and the bivariate normal model. -- Conjugate prior for the bivariate regression model. -- Comparison of several means- the one way model. -- The two way layout. -- The general linear model. -- Other topics. The likelihood principle. -- The stopping rule principle. -- Informative stopping rules. -- The likelihood principle and reference priors. -- Bayesian decision theory. -- Bayes linear methods. -- Decision theory and hypothesis testing. -- Empirical Bayes methods. -- Hierarchical models. The idea of a hierarchical model. -- The hierarchical normal model. -- The baseball example. -- The Stein estimator. -- Bayesian analysis for an unknown overall mean. -- The general linear model revisited. -- The Gibbs sampler and other numerical methods. Introduction to numerical methods. -- The EM algorithm. -- Data augmentation by Monte Carlo. -- The Gibbs sampler. -- Rejection sampling. -- The Metropolis-Hastings algorithm. -- Introduction to WinBUGS and OpenBUGS. -- Generalized linear models. -- Some approximate methods. Bayesian importance sampling. -- Variational Bayesian methods: simple case. -- Variational Bayesian methods: general case. -- ABC: approximate Bayesian computation. -- Reversible jump Markov chain Monte Carlo.

"--Presents extensive examples throughout the book to complement the theory presented. Includes significant new material on recent techniques such as variational methods, importance sampling, approximate computation and reversible jump MCMC".

Texto en inglés