عنوان

Spatial and spatio-temporal Bayesian models with R-INLA /

شابک

1118326555

شابک

1118950194

شابک

1118950208

شابک

1118950216

شابک

9781118326558

شابک

9781118950197

شابک

9781118950203

شابک

9781118950210

شابک اشتباه

9781118326558

شماره

dltt

عنوان اصلي

Spatial and spatio-temporal Bayesian models with R-INLA /

نام عام مواد

[Book]

نام نخستين پديدآور

by Marta Blangiardo and Michela Cameletti

نام خاص و کميت اثر

1 online resource

متن يادداشت

Includes bibliographical references and index

متن يادداشت

Introduction -- Why spatial and spatio-temporal statistics? -- Why do we use Bayesian methods for modeling spatial and spatio-temporal structures? -- Why INLA?p3 -- Datasets -- National Morbidity, Mortality, and Air Pollution Study -- Average income in Swedish municipalities -- Stroke in Sheffield -- Ship accidents -- CD4 in HIV patients -- Lip cancer in Scotland -- Suicides in London -- Brian cancer in Navarra, Spain -- Respiratory hospital admission in Turin province -- Malaria in the Gambia -- Swiss rainfall data -- Lung cancer mortality in Ohio -- Low birth weight births in Georgia -- Air pollution in Piemonte -- Introduction to R -- The R language -- R objects -- Data and session management -- Packages -- Programming in R -- Basic statistical analysis with R -- Introduction to Bayesian methods -- Bayesian philosophy -- Thomas Bayes and Simon Pierre Laplace -- Bruno de Finetti and colleagues -- After the Second World War -- The 1990s and beyond -- Basic probability elements -- What is an event? -- Probability of events -- Conditional probability -- Bayes theorem -- Prior and posterior distributions -- Bayesian inference -- Working with the posterior distribution -- Choosing the prior distribution -- Type of distribution -- Conjugacy -- Noninformative or informative prior -- Bayesian computing -- Monte Carlo integration -- Monte Carlo method for Bayesian inference -- Probability distributions and random number generation in R -- Examples of Monte Carlo simulation -- Markov chain Monte Carlo methods -- Gibbs sampler -- Metropolis - Hastings algorithm -- MCMC implementation: software and output analysis -- The integrated nested Laplace approximations algorithm -- Laplace approximation -- INLA setting: the class of latent Gaussian models -- Approximate Bayesian inference with INLA -- The R - INLA package -- How INLA works: step-by-step example -- Bayesian regression and hierarchical models -- Linear regression -- Comparing the Bayesian to the classical regression model -- Example: studying the relationship between temperature and PM10 -- Nonlinear regression: random walk -- Example: studying the relationship between average household age and income in Sweden -- Generalized linear models -- Hierarchical models -- Exchangeability -- INLA as a hierarchical model -- Hierarchical regression -- Example: a hierarchical model for studying CD4 counts in AIDS patients -- Example: a hierarchical model for studying lip cancer in Scotland -- Example: studying stroke mortality in Sheffield (UK) -- Prediction -- Model checking and selection -- Methods based on the predictive distribution -- Methods based on the deviance -- Spatial modeling -- Areal data - GMRF -- Disease mapping -- BYM model: suicides in London -- Ecological regression -- Zero-inflated models -- Zero-inflated Poisson model: brain cancer in Navarra -- Zero-inflated binomial model: air pollution and respiratory hospital admissions -- Geostatistical data -- The stochastic partial differential equation approach -- Nonstationary Gaussian field -- SPDE within R - INLA -- SPDE toy example with simulated data -- Mesh construction -- The observation or projector matrix -- More advanced operations through the inla stack function -- Spatial prediction -- Prior specification for the stationary case -- Example with simulated data -- SPDE for Gaussian response: Swiss rainfall data -- SPDE with nonnormal outcome: malaria in the Gambia -- Prior specification for the nonstationary case -- Example with simulated data -- Spatio-temporal models -- Spatio-temporal disease mapping -- Nonparametric dynamic trend -- Space-time interactions -- Spatio-temporal modeling particulate matter concentration -- Change of support -- Advanced modeling / Elias T. Krainski -- Bivariate model for spatially misaligned data -- Bivariate model for spatially misaligned data -- Joint model with Gaussian distributions -- Joint model with non-Gaussian distributions -- Semicontinuous model to daily rainfall -- Spatio-temporal dynamic models -- Dynamic model with Besag proper specification -- Dynamic model with generic specification -- Space-time model lowering the time resolution -- Spatio-temporal model

بدون عنوان

0

عنوان

Spatial and spatio-temporal Bayesian models with R-INLA.

شماره استاندارد بين المللي کتاب و موسيقي

9781118326558

موضوع مستند نشده

Asymptotic distribution (Probability theory)

موضوع مستند نشده

Bayesian statistical decision theory.

موضوع مستند نشده

R (Computer program language)

موضوع مستند نشده

Spatial analysis (Statistics)

موضوع مستند نشده

MAT-- 003000

موضوع مستند نشده

MAT-- 029000

شماره

ويراست

شماره رده

مستند نام اشخاص تاييد نشده

Blangiardo, Marta.

مستند نام اشخاص تاييد نشده

Cameletti, Michela.

مستند نام تنالگان تاييد نشده

Ohio Library and Information Network.

تاريخ عمليات

20160603082853.9

قواعد فهرست نويسي ( بخش توصيفي )

pn

نام الکترونيکي

نوع ماده

[Book]

تكميل شده

Y