عنوان

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

1118326555

1118950194

1118950208

1118950216

9781118326558

9781118950197

9781118950203

9781118950210

9781118326558

dltt

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

[Book]

by Marta Blangiardo and Michela Cameletti

1 online resource

Includes bibliographical references and index

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

0

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

9781118326558

Asymptotic distribution (Probability theory)

Bayesian statistical decision theory.

R (Computer program language)

Spatial analysis (Statistics)

MAT-- 003000

MAT-- 029000

Blangiardo, Marta.

Cameletti, Michela.

Ohio Library and Information Network.

20160603082853.9

pn

[Book]

Y