![]() |
Welcome to Loot.co.za!
Sign in / Register |Wishlists & Gift Vouchers |Help | Advanced search
|
Your cart is empty |
||
Showing 1 - 3 of 3 matches in All Departments
The use of computational methods in statistics to face complex problems and highly dimensional data, as well as the widespread availability of computer technology, is no news. The range of applications, instead, is unprecedented. As often occurs, new and complex data types require new strategies, demanding for the development of novel statistical methods and suggesting stimulating mathematical problems. This book is addressed to researchers working at the forefront of the statistical analysis of complex systems and using computationally intensive statistical methods.
The use of computational methods in statistics to face complex problems and highly dimensional data, as well as the widespread availability of computer technology, is no news. The range of applications, instead, is unprecedented. As often occurs, new and complex data types require new strategies, demanding for the development of novel statistical methods and suggesting stimulating mathematical problems. This book is addressed to researchers working at the forefront of the statistical analysis of complex systems and using computationally intensive statistical methods.
State space models have gained tremendous popularity in recent years in as disparate fields as engineering, economics, genetics and ecology. After a detailed introduction to general state space models, this book focuses on dynamic linear models, emphasizing their Bayesian analysis. Whenever possible it is shown how to compute estimates and forecasts in closed form; for more complex models, simulation techniques are used. A final chapter covers modern sequential Monte Carlo algorithms. The book illustrates all the fundamental steps needed to use dynamic linear models in practice, using R. Many detailed examples based on real data sets are provided to show how to set up a specific model, estimate its parameters, and use it for forecasting. All the code used in the book is available online. No prior knowledge of Bayesian statistics or time series analysis is required, although familiarity with basic statistics and R is assumed.
|
You may like...
Management Of Information Security
Michael Whitman, Herbert Mattord
Paperback
Advanced Nanoscale ULSI Interconnects…
Yosi Shacham-Diamand, Tetsuya Osaka, …
Hardcover
R4,132
Discovery Miles 41 320
Creativity in Computing and DataFlow…
Suyel Namasudra, Veljko Milutinovic
Hardcover
R4,204
Discovery Miles 42 040
Better Choices - Ensuring South Africa's…
Greg Mills, Mcebisi Jonas, …
Paperback
12 Rules For Life - An Antidote To Chaos
Jordan B. Peterson
Paperback
(2)
|