| 000 | 00000cam u2200205 a 4500 | |
| 001 | 000046001425 | |
| 005 | 20191011103430 | |
| 008 | 191007s2019 flua b 001 0 eng d | |
| 010 | ▼a 2018047838 | |
| 020 | ▼a 9781138369856 (hardback : alk. paper) | |
| 020 | ▼z 9780429031892 (ebook) | |
| 035 | ▼a (KERIS)REF000018834761 | |
| 040 | ▼a DLC ▼b eng ▼e rda ▼c DLC ▼d 211009 | |
| 050 | 0 0 | ▼a QA274.23 ▼b .A38 2019 |
| 082 | 0 0 | ▼a 519.2/2 ▼2 23 |
| 084 | ▼a 519.22 ▼2 DDCK | |
| 090 | ▼a 519.22 ▼b A244 | |
| 245 | 0 0 | ▼a Advanced spatial modeling with stochastic partial differential equations using R and INLA / ▼c E.T. Krainski ... [et al.]. |
| 246 | 3 | ▼a Advanced spatial modeling with stochastic partial differential equations using R and integrated nested Laplace approximation |
| 260 | ▼a Boca Raton : ▼b CRC Press, Taylor & Francis Group, ▼c c2019. | |
| 300 | ▼a xiii, 283 p. : ▼b ill. ; ▼c 25 cm. | |
| 504 | ▼a Includes bibliographical references and index. | |
| 650 | 0 | ▼a Stochastic differential equations. |
| 650 | 0 | ▼a Mathematical models. |
| 650 | 0 | ▼a Stochastic processes. |
| 650 | 0 | ▼a Laplace transformation. |
| 650 | 0 | ▼a R (Computer program language). |
| 700 | 1 | ▼a Krainski, E. T. ▼q (Elias T.). |
| 945 | ▼a KLPA |
소장정보
| No. | 소장처 | 청구기호 | 등록번호 | 도서상태 | 반납예정일 | 예약 | 서비스 |
|---|---|---|---|---|---|---|---|
| No. 1 | 소장처 중앙도서관/서고7층/ | 청구기호 519.22 A244 | 등록번호 111815872 | 도서상태 대출가능 | 반납예정일 | 예약 | 서비스 |
컨텐츠정보
목차
Preamble
What this book is and isn’t
- The Integrated Nested Laplace Approximation and the R-INLA package
Introduction
The INLA method
A simple example
Additional arguments and control options
Manipulating the posterior marginals
Advanced features
- Introduction to spatial modeling
Introduction
The SPDE approach
A toy example
Projection of the random field
Prediction
Triangulation details and examples
Tools for mesh assessment
Non-Gaussian response: Precipitation in Parana
- More than one likelihood
Coregionalization model
Joint modeling: Measurement error model
Copying part of or the entire linear predictor
- Point processes and preferential sampling
Introduction
Including a covariate in the log-Gaussian Cox process
Geostatistical inference under preferential sampling
- Spatial non-stationarity
Explanatory variables in the covariance
The Barrier model
Barrier model for noise data in Albacete (Spain)
- Risk assessment using non-standard likelihoods
Survival analysis
Models for extremes
- Space-time models
Discrete time domain
Continuous time domain
Lowering the resolution of a spatio-temporal model
Conditional simulation: Combining two meshes
- Space-time applications
Space-time coregionalization model
Dynamic regression example
Space-time point process: Burkitt example
Large point process dataset
Accumulated rainfall: Hurdle Gamma model
List of symbols and notation
Packages used in the book
정보제공 :
