Likelihood-Free Methods for Cognitive Science, James J. Palestro; Per B. Sederberg; Adam F. Osth;
Автор: P.P.B. Eggermont; V.N. LaRiccia Название: Maximum Penalized Likelihood Estimation ISBN: 1441929282 ISBN-13(EAN): 9781441929280 Издательство: Springer Рейтинг: Цена: 25853.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book deals with parametric and nonparametric density estimation from the maximum (penalized) likelihood point of view, including estimation under constraints.
Автор: James J. Palestro; Per B. Sederberg; Adam F. Osth; Название: Likelihood-Free Methods for Cognitive Science ISBN: 3319891812 ISBN-13(EAN): 9783319891811 Издательство: Springer Рейтинг: Цена: 12577.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
This book explains the foundation of approximate Bayesian computation (ABC), an approach to Bayesian inference that does not require the specification of a likelihood function. As a result, ABC can be used to estimate posterior distributions of parameters for simulation-based models. Simulation-based models are now very popular in cognitive science, as are Bayesian methods for performing parameter inference. As such, the recent developments of likelihood-free techniques are an important advancement for the field. Chapters discuss the philosophy of Bayesian inference as well as provide several algorithms for performing ABC. Chapters also apply some of the algorithms in a tutorial fashion, with one specific application to the Minerva 2 model. In addition, the book discusses several applications of ABC methodology to recent problems in cognitive science. Likelihood-Free Methods for Cognitive Science will be of interest to researchers and graduate students working in experimental, applied, and cognitive science.
Автор: Martin A. Tanner Название: Tools for Statistical Inference ISBN: 0387946888 ISBN-13(EAN): 9780387946887 Издательство: Springer Рейтинг: Цена: 13969.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book provides a unified introduction to a variety of computational algorithms for likelihood and Bayesian inference. The third edition expands the discussion of many of the techniques discussed, includes additional examples, and adds exercise sets at the end of each chapter.
Автор: Owen Название: Empirical Likelihood ISBN: 1584880716 ISBN-13(EAN): 9781584880714 Издательство: Taylor&Francis Рейтинг: Цена: 20671.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Applies empirical likelihood method to problems ranging from those as simple as setting a confidence region for a univariate mean under IID sampling, to problems defined through smooth functions of means, regression models, generalized linear models, estimating equations, or kernel smooths, and to sampling with non-identically distributed data.
Автор: Daniel Sorensen; Daniel Gianola Название: Likelihood, Bayesian, and MCMC Methods in Quantitative Genetics ISBN: 1441929975 ISBN-13(EAN): 9781441929976 Издательство: Springer Рейтинг: Цена: 26120.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book, suitable for numerate biologists and for applied statisticians, provides the foundations of likelihood, Bayesian and MCMC methods in the context of genetic analysis of quantitative traits.
Автор: Vexler, Albert Название: Empirical Likelihood Methods in Biomedicine and Health ISBN: 1032401818 ISBN-13(EAN): 9781032401812 Издательство: Taylor&Francis Рейтинг: Цена: 7348.00 р. Наличие на складе: Поставка под заказ.
Автор: Lee, Youngjo (Seoul National University, South Korea) Nelder, John A. Pawitan, Yudi (Karolinska Institute, Stockholm, Sweden) Название: Generalized Linear Models with Random Effects ISBN: 1032096632 ISBN-13(EAN): 9781032096636 Издательство: Taylor&Francis Рейтинг: Цена: 7501.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This is the second edition of a monograph on generalized linear models with random effects that extends the classic work of McCullagh and Nelder. It has been thoroughly updated, with around 80 pages added, including new material on the extended likelihood approach that strengthens the theoretical basis of the methodology, new developments in var
Автор: Bohning, Dankmar Название: Meta-analysis of Binary Data Using Profile Likelihood ISBN: 1584886307 ISBN-13(EAN): 9781584886303 Издательство: Taylor&Francis Рейтинг: Цена: 24499.00 р. Наличие на складе: Поставка под заказ.
Автор: Klima, Richard (appalachian State University, Boone, North Carolina, Usa) Klima, Richard E. (appalachian State University, Boone, North Carolina, Usa) Название: Advanced applications in remote sensing of agricultural crops and natural vegetation ISBN: 1032478004 ISBN-13(EAN): 9781032478005 Издательство: Taylor&Francis Рейтинг: Цена: 6583.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book redresses the balance, explaining why science has clung to a defective methodology despite its well-known defects. After examining the strengths and weaknesses of the work of Neyman and Pearson and the Fisher paradigm, the author proposes an alternative paradigm.
Автор: Pawitan, Yudi (professor Of Statistics, Department Of Statistics, National University Of Ireland, Cork) Название: In all likelihood ISBN: 0199671222 ISBN-13(EAN): 9780199671229 Издательство: Oxford Academ Рейтинг: Цена: 9504.00 р. Наличие на складе: Поставка под заказ.
Автор: Chambers, Raymond L. Название: Maximum Likelihood Estimation for Sample Surveys ISBN: 1584886323 ISBN-13(EAN): 9781584886327 Издательство: Taylor&Francis Рейтинг: Цена: 24499.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Paul P. Eggermont; Vincent N. LaRiccia Название: Maximum Penalized Likelihood Estimation ISBN: 1461417120 ISBN-13(EAN): 9781461417125 Издательство: Springer Рейтинг: Цена: 23058.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Ideal for researchers and practitioners in statistics and industrial mathematics, this book covers the theory and practice of nonparametric estimation. It is novel in its use of maximum penalized likelihood estimation and convex minimization problem theory.
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