Aug 28, 2013

Two forthcoming titles in IMS Textbooks series

Cambridge University Press, in conjunction with the Institute of Mathematical Statistics, publishes IMS Textbooks. These are compact books that give introductory accounts of topics from statistics or probability of current interest, suitable for advanced courses at master’s level, for doctoral students and for individual study. They are shorter than a fully developed textbook, and typically contain exercises. The Series Editors are Susan Holmes, Ben Hambly, Alan Agresti, D. R. Cox, and Xiao-Li Meng. See http://www.cambridge.org/ar/academic/subjects/statistics-probability/statistics-and-probability-general-interest/series/institute-mathematical-statistics-textbooks

There are two forthcoming titles in the IMS Textbooks series, which are currently [August 2013] available for pre-order.

Stochastic Networks
Frank Kelly, University of Cambridge, and Elena Yudovina, University of Michigan, Ann Arbor.

This textbook is derived from a graduate-level course taught at the University of Cambridge. Self-contained appendices contain discussion of relevant mathematical techniques, e.g. Lagrange multipliers.
Priced at $99 hardback, $36.99 paperback, but IMS members will receive 40% discount. Order details to follow.

Bayesian Filtering and Smoothing
Simo Särkkä, Aalto University, Finland

Filtering and smoothing methods are used to produce an accurate estimate of the state of a time-varying system based on multiple observational inputs (data). Interest in these methods has exploded in recent years, with numerous applications emerging in fields such as navigation, aerospace engineering, telecommunications and medicine. This compact, informal introduction for graduate students and advanced undergraduates presents the current state-of-the-art filtering and smoothing methods in a unified Bayesian framework. Readers learn what non-linear Kalman filters and particle filters are, how they are related, and their relative advantages and disadvantages. They also discover how state-of-the-art Bayesian parameter estimation methods can be combined with state-of-the-art filtering and smoothing algorithms. The book’s practical and algorithmic approach assumes only modest mathematical prerequisites. Examples include MATLAB computations, and the numerous end-of-chapter exercises include computational assignments. MATLAB/GNU Octave source code is available for download at www.cambridge.org/sarkka, promoting hands-on work with the methods.
Priced at $99 hardback, $36.99 paperback; IMS members will receive 40% discount off these prices. Ordering details to follow.

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