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Spatial and Spatio-temporal Bayesian Models with R - INLA
Marta Blangiardo
€ 74.13
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Description for Spatial and Spatio-temporal Bayesian Models with R - INLA
Hardcover. Num Pages: 320 pages, black & white illustrations. BIC Classification: PBT. Category: (P) Professional & Vocational. Dimension: 235 x 150 x 20. Weight in Grams: 534.
Spatial and Spatio-Temporal Bayesian Models with R-INLA provides a much needed, practically oriented & innovative presentation of the combination of Bayesian methodology and spatial statistics. The authors combine an introduction to Bayesian theory and methodology with a focus on the spatial and spatio-temporal models used within the Bayesian framework and a series of practical examples which allow the reader to link the statistical theory presented to real data problems. The numerous examples from the fields of epidemiology, biostatistics and social science all are coded in the R package R-INLA, which has proven to be a valid alternative to the commonly ... Read more
Spatial and Spatio-Temporal Bayesian Models with R-INLA provides a much needed, practically oriented & innovative presentation of the combination of Bayesian methodology and spatial statistics. The authors combine an introduction to Bayesian theory and methodology with a focus on the spatial and spatio-temporal models used within the Bayesian framework and a series of practical examples which allow the reader to link the statistical theory presented to real data problems. The numerous examples from the fields of epidemiology, biostatistics and social science all are coded in the R package R-INLA, which has proven to be a valid alternative to the commonly ... Read more
Product Details
Publisher
Wiley
Format
Hardback
Publication date
2015
Condition
New
Number of Pages
320
Place of Publication
New York, United States
ISBN
9781118326558
SKU
V9781118326558
Shipping Time
Usually ships in 4 to 8 working days
Ref
99-1
About Marta Blangiardo
Marta Blangiardo, MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, Imperial College London, UK Michela Cameletti, Department of Management, Economics and Quantitative Methods, University of Bergamo, Italy
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