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05242nam a2200781 4500 |
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ocn900278105 |
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OCoLC |
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20170124070357.6 |
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|2 23
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| 049 |
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|a MAIN
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1 |
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|a Blangiardo, Marta.
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|a Spatial and spatio-temporal Bayesian models with R-INLA /
|c by Marta Blangiardo and Michela Cameletti.
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| 264 |
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1 |
|a Chichester, West Sussex :
|b John Wiley and Sons, Inc.,
|c 2015.
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| 300 |
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|a 1 online resource.
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 504 |
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|a Includes bibliographical references and index.
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|a Print version record and CIP data provided by publisher; resource not viewed.
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|a Title Page; Copyright; Table of Contents; Dedication; Preface; Chapter 1: Introduction; 1.1 Why spatial and spatio-temporal statistics?; 1.2 Why do we use Bayesian methods for modeling spatial and spatio-temporal structures?; 1.3 Why INLA?; 1.4 Datasets; References; Chapter 2: Introduction to R; 2.1 The R language; 2.2 R objects; 2.3 Data and session management; 2.4 Packages; 2.5 Programming in R; 2.6 Basic statistical analysis with R; References; Chapter 3: Introduction to Bayesian methods; 3.1 Bayesian philosophy; 3.2 Basic probability elements; 3.3 Bayes theorem
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|a 3.4 Prior and posterior distributions3.5 Working with the posterior distribution; 3.6 Choosing the prior distribution; References; Chapter 4: Bayesian computing; 4.1 Monte Carlo integration; 4.2 Monte Carlo method for Bayesian inference; 4.3 Probability distributions and random number generation in R; 4.4 Examples of Monte Carlo simulation; 4.5 Markov chain Monte Carlo methods; 4.6 The integrated nested Laplace approximations algorithm; 4.7 Laplace approximation; 4.8 The R-INLA package; 4.9 How INLA works: step-by-step example; References
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| 505 |
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|a Chapter 5: Bayesian regression and hierarchical models5.1 Linear regression; 5.2 Nonlinear regression: random walk; 5.3 Generalized linear models; 5.4 Hierarchical models; 5.5 Prediction; 5.6 Model checking and selection; References; Chapter 6: Spatial modeling; 6.1 Areal data -- GMRF; 6.2 Ecological regression; 6.3 Zero-inflated models; 6.4 Geostatistical data; 6.5 The stochastic partial differential equation approach; 6.6 SPDE within R-INLA; 6.7 SPDE toy example with simulated data; 6.8 More advanced operations through the inla.stack function; 6.9 Prior specification for the stationary case
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|a 6.10 SPDE for Gaussian response: Swiss rainfall data6.11 SPDE with nonnormal outcome: malaria in the Gambia; 6.12 Prior specification for the nonstationary case; References; Chapter 7: Spatio-temporal models; 7.1 Spatio-temporal disease mapping; 7.2 Spatio-temporal modeling particulate matter concentration; References; Chapter 8: Advanced modeling; 8.1 Bivariate model for spatially misaligned data; 8.2 Semicontinuous model to daily rainfall; 8.3 Spatio-temporal dynamic models; 8.4 Space-time model lowering the time resolution; References; Index; End User License Agreement
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| 650 |
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0 |
|a Bayesian statistical decision theory.
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| 650 |
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0 |
|a Spatial analysis (Statistics)
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| 650 |
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0 |
|a Asymptotic distribution (Probability theory)
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| 650 |
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0 |
|a R (Computer program language)
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| 650 |
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7 |
|a MATHEMATICS
|x Applied.
|2 bisacsh
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| 650 |
|
7 |
|a MATHEMATICS
|x Probability & Statistics
|x General.
|2 bisacsh
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| 650 |
|
7 |
|a Asymptotic distribution (Probability theory)
|2 fast
|0 (OCoLC)fst00819866
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| 650 |
|
7 |
|a Bayesian statistical decision theory.
|2 fast
|0 (OCoLC)fst00829019
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| 650 |
|
7 |
|a R (Computer program language)
|2 fast
|0 (OCoLC)fst01086207
|
| 650 |
|
7 |
|a Spatial analysis (Statistics)
|2 fast
|0 (OCoLC)fst01128784
|
| 655 |
|
4 |
|a Electronic books.
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| 700 |
1 |
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|a Cameletti, Michela.
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| 776 |
0 |
8 |
|i Print version:
|a Blangiardo, Marta.
|t Spatial and spatio-temporal Bayesian models with R-INLA.
|d Chichester, West Sussex : John Wiley and Sons, Inc., 2015
|z 9781118326558
|w (DLC) 2015000696
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| 856 |
4 |
0 |
|u https://doi.org/10.1002/9781118950203
|z Full Text via HEAL-Link
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| 994 |
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|a 92
|b DG1
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