Highlighting modern computational methods, Applied Stochastic Modelling, Second Edition provides students with the practical experience of scientific computing in applied statistics through a range of interesting real-world applications. It also successfully revises standard probability and statistical theory. Along with an updated bibliography and
Rezensionen / Stimmen
Praise for the First Edition
The author's enthusiasm for his subject shines through this book. There are plenty of interesting example data sets ... The book covers much ground in quite a short space ... In conclusion, I like this book and strongly recommend it. It covers many of my favourite topics. In another life, I would have liked to have written it, but Professor Morgan has made a better job if it than I would have done.
-Tim Auton, Journal of the Royal Statistical Society
I am seriously considering adopting Applied Stochastic Modelling for a graduate course in statistical computation that our department is offering next term.
-Jim Albert, Journal of the American Statistical Association
...very well written, fresh in its style, with lots of wonderful examples and problems.
-R.P. Dolrow, Technometrics
A useful tool for both applied statisticians and stochastic model users of other fields, such as biologists, sociologists, geologists, and economists.
-Zentralblatt MATH
The book is a delight to read, reflecting the author's enthusiasm for the subject and his wide experience. The layout and presentation of material are excellent. Both for new research students and for experienced researchers needing to update their skills, this is an excellent text and source of reference.
-Statistical Methods in Medical Research
Reihe
Auflage
Sprache
Verlagsort
Verlagsgruppe
Zielgruppe
Für höhere Schule und Studium
Undergraduate and graduate students in mathematics and statistics.
Editions-Typ
Illustrationen
73 s/w Abbildungen
73 b/w images
ISBN-13
978-1-4200-1165-4 (9781420011654)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
Introduction and Examples. Basic Model Fitting. Function Optimisation. Basic Likelihood Tools. General Principles. Simulation Techniques. Bayesian Methods and MCMC. General Families of Models. Index of Data Sets. Index of MATLAB Programs. Appendices. Solutions and Comments for Selected Exercises. Bibliography. Index.
