Applied Statistical Modelling for Ecologists: A Practical Guide to Bayesian and Likelihood Inference Using R, JAGS, NIMBLE, Stan and TMB
Autor Marc Kéry, Kenneth F. Kellneren Limba Engleză Paperback – 18 iul 2024
- Contains a concise and gentle introduction to probability and applied statistics as needed in ecology and the environmental sciences
- Covers the foundations of modern applied statistical modelling
- Gives a comprehensive, applied introduction to what currently are the most widely used and most exciting, cutting-edge model fitting software packages: JAGS, NIMBLE, Stan, and TMB
- Provides a highly accessible applied introduction to the two dominant methods of fitting parametric statistical models: maximum likelihood and Bayesian posterior inference
- Details the principles of model building, model checking and model selection
- Adopts a “Rosetta Stone” approach, wherein understanding of one software, and of its associated language, will be greatly enhanced by seeing the analogous code in other engines
- Provides all code available for download for students, at https://www.elsevier.com/books-and-journals/book-companion/9780443137150
Preț: 421.48 lei
Preț vechi: 538.08 lei
-22% Nou
Puncte Express: 632
Preț estimativ în valută:
80.65€ • 84.85$ • 66.95£
80.65€ • 84.85$ • 66.95£
Carte tipărită la comandă
Livrare economică 08-22 ianuarie 25
Livrare express 11-17 decembrie pentru 91.26 lei
Preluare comenzi: 021 569.72.76
Specificații
ISBN-13: 9780443137150
ISBN-10: 0443137153
Pagini: 550
Dimensiuni: 191 x 235 x 30 mm
Greutate: 1.1 kg
Editura: ELSEVIER SCIENCE
ISBN-10: 0443137153
Pagini: 550
Dimensiuni: 191 x 235 x 30 mm
Greutate: 1.1 kg
Editura: ELSEVIER SCIENCE
Cuprins
1. Introduction 2. Introduction to statistical inference 3. Linear regression models and their extensions to generalized linear, hierarchical and integrated models 4. Introduction to general-purpose model-fitting engines and the model of the mean 5. Simple linear regression with Normal errors 6. Comparison of two groups 7. Comparisons among multiple groups 8. Comparisons in two classifications or with two categorical covariates 9. General linear model with continuous and categorical explanatory variables 10. Linear mixed-effects model 11. Introduction to the Generalized linear model (GLM): Comparing two groups in a Poisson regression 12. Overdispersion, zero-inflation and offsets in a GLM 13. Poisson regression with both continuous and categorical explanatory variables 14. Poisson mixed-effects model or Poisson GLMM 15. Comparing two groups in a Binomial regression 16. Binomial GLM with both continuous and categorical explanatory variables 17. Binomial mixed-effects model or Binomial GLMM 18. Model building, model checking and model selection 19. General hierarchical models: Site-occupancy species distribution model (SDM) 20. Integrated models 21. Conclusion