عنوان

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

(Number (ISBN

1118326555

(Number (ISBN

1118950194

(Number (ISBN

1118950208

(Number (ISBN

1118950216

(Number (ISBN

9781118326558

(Number (ISBN

9781118950197

(Number (ISBN

9781118950203

(Number (ISBN

9781118950210

Erroneous ISBN

9781118326558

Number

dltt

Title Proper

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

General Material Designation

[Book]

First Statement of Responsibility

by Marta Blangiardo and Michela Cameletti

Specific Material Designation and Extent of Item

1 online resource

Text of Note

Includes bibliographical references and index

Text of Note

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

Title

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

International Standard Book Number

9781118326558

Asymptotic distribution (Probability theory)

Bayesian statistical decision theory.

R (Computer program language)

Spatial analysis (Statistics)

MAT-- 003000

MAT-- 029000

Number

Edition

Class number

Blangiardo, Marta.

Cameletti, Michela.

Ohio Library and Information Network.

Date of Transaction

20160603082853.9

Cataloguing Rules (Descriptive Conventions))

pn

Electronic name

[Book]

Y