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02855aam a2200385Ki 4500 001 ADA41386A55F11EAA027EF1497128E48 003 SILO 005 20200603010033 008 191019s2020 flua b 001 0 eng d 020 $a 113803987X 020 $a 9781138039872 035 $a (OCoLC)1123185987 040 $a YDX $b eng $e rda $c YDX $d UKMGB $d OCLCF $d YDXIT $d SILO 050 4 $a QA279.5 $b .G66 2020 082 04 $a 519.542 $2 23 100 1 $a GoÌmez-Rubio, Virgilio $e author. 245 10 $a Bayesian inference with INLA / $c Virgilio GoÌmez-Rubio. 264 1 $a Boca Raton, FL : $b CRC Press, $c [2020] 300 $a xiii, 315 pages ; $c 26 cm 504 $a Includes bibliographical references and index. 520 $a The integrated nested Laplace approximation (INLA) is a recent computational method that can fit Bayesian models in a fraction of the time required by typical Markov chain Monte Carlo (MCMC) methods. INLA focuses on marginal inference on the model parameters of latent Gaussian Markov random fields models and exploits conditional independence properties in the model for computational speed. Bayesian Inference with INLA provides a description of INLA and its associated R package for model fitting. This book describes the underlying methodology as well as how to fit a wide range of models with R. Topics covered include generalized linear mixed-effects models, multilevel models, spatial and spatio-temporal models, smoothing methods, survival analysis, imputation of missing values, and mixture models. Advanced features of the INLA package and how to extend the number of priors and latent models available in the package are discussed. All examples in the book are fully reproducible and datasets and R code are available from the book website. This book will be helpful to researchers from different areas with some background in Bayesian inference that want to apply the INLA method in their work. The examples cover topics on biostatistics, econometrics, education, environmental science, epidemiology, public health, and the social sciences. 650 0 $a Bayesian statistical decision theory. 650 0 $a Laplace transformation. 650 0 $a Regression analysis. 650 0 $a Markov processes. 650 0 $a Gaussian processes. 650 7 $a Bayesian statistical decision theory. $2 fast $0 (OCoLC)fst00829019 650 7 $a Laplace transformation. $2 fast $0 (OCoLC)fst00992599 650 7 $a Markov processes. $2 fast $0 (OCoLC)fst01010347 650 7 $a Monte Carlo method. $2 fast $0 (OCoLC)fst01025819 776 08 $i Electronic version: $a GoÌmez-Rubio, Virgilio. $t Bayesian inference with INLA. $d [Boca Raton, FL] : CRC Press, 2020 $z 9781351707190 $w (OCoLC)1141927806 941 $a 1 952 $l USUX851 $d 20221103011413.0 956 $a http://locator.silo.lib.ia.us/search.cgi?index_0=id&term_0=ADA41386A55F11EAA027EF1497128E48 994 $a 92 $b IWAInitiate Another SILO Locator Search