Author: North-Holland Publishing CompanyPublisher:
ISBN:
Category :
Languages : en
Pages : 812
Book Description
Journal of Statistical Planning and Inference
Author: North-Holland Publishing CompanyPublisher:
ISBN:
Category :
Languages : en
Pages : 812
Book Description
Statistical Inference in Stochastic Processes
Author: Ishwar V. BasawaStatistical Planning and Inference
Author: Subir GhoshPublisher: Wiley
ISBN: 9781119962786
Category : Science
Languages : en
Pages : 512
Book Description
This book introduces statistical planning and inference, presenting both classical theory and the major developments in the field. Each chapter presents problems and their solutions along with illustrative examples to introduce concepts and methods, and is supported by a supplementary website featuring guidance on how to implement methods using R.
Innovations and Advanced Techniques in Systems, Computing Sciences and Software Engineering
Author: Khaled ElleithyPublisher: Springer Science & Business Media
ISBN: 1402087357
Category : Computers
Languages : en
Pages : 580
Book Description
Innovations and Advanced Techniques in Systems, Computing Sciences and Software Engineering includes a set of rigorously reviewed world-class manuscripts addressing and detailing state-of-the-art research projects in the areas of Computer Science, Software Engineering, Computer Engineering, and Systems Engineering and Sciences. Innovations and Advanced Techniques in Systems, Computing Sciences and Software Engineering includes selected papers form the conference proceedings of the International Conference on Systems, Computing Sciences and Software Engineering (SCSS 2007) which was part of the International Joint Conferences on Computer, Information and Systems Sciences and Engineering (CISSE 2007).
Journal of Statistical Planning and Inference
Author: North-Holland Publishing CompanyPublisher:
ISBN:
Category :
Languages : en
Pages : 1216
Book Description
Statistics '91 Canada Conference Papers
Author: Canada. StatisticsJournal of Statistical Planning and Inference
Author: Journal of statistical planning and inference
Author: Elsevier Science (Firm)Bayesian Nonparametrics
Author: Nils Lid HjortPublisher: Cambridge University Press
ISBN: 1139484605
Category : Mathematics
Languages : en
Pages : 309
Book Description
Bayesian nonparametrics works - theoretically, computationally. The theory provides highly flexible models whose complexity grows appropriately with the amount of data. Computational issues, though challenging, are no longer intractable. All that is needed is an entry point: this intelligent book is the perfect guide to what can seem a forbidding landscape. Tutorial chapters by Ghosal, Lijoi and Prünster, Teh and Jordan, and Dunson advance from theory, to basic models and hierarchical modeling, to applications and implementation, particularly in computer science and biostatistics. These are complemented by companion chapters by the editors and Griffin and Quintana, providing additional models, examining computational issues, identifying future growth areas, and giving links to related topics. This coherent text gives ready access both to underlying principles and to state-of-the-art practice. Specific examples are drawn from information retrieval, NLP, machine vision, computational biology, biostatistics, and bioinformatics.
Matrix Tricks for Linear Statistical Models
Author: Simo PuntanenPublisher: Springer Science & Business Media
ISBN: 3642104738
Category : Mathematics
Languages : en
Pages : 486
Book Description
In teaching linear statistical models to first-year graduate students or to final-year undergraduate students there is no way to proceed smoothly without matrices and related concepts of linear algebra; their use is really essential. Our experience is that making some particular matrix tricks very familiar to students can substantially increase their insight into linear statistical models (and also multivariate statistical analysis). In matrix algebra, there are handy, sometimes even very simple “tricks” which simplify and clarify the treatment of a problem—both for the student and for the professor. Of course, the concept of a trick is not uniquely defined—by a trick we simply mean here a useful important handy result. In this book we collect together our Top Twenty favourite matrix tricks for linear statistical models.