Intermediate Accounting or Markov Decision Processes

Intermediate Accounting

Author: Donald E Kieso

Keeping its finger on the pulse of the profession, the new twelfth edition update of this bestselling book effectively prepares readers for their accounting futures. They'll find the latest information in the field, including Sarbanes-Oxley Act legislation as well as proven tips for passing the computerized CPA exam. Reflecting the demands for entry-level accountants, the focus of this book is on fostering critical thinking skills, reducing emphasis on memorization and encouraging more analysis and interpretation by requiring use of technology tools, spreadsheets and databases. It integrates numerous examples from real corporations throughout the chapters to clearly demonstrate how accounting principles and techniques are applied in practice.

Booknews

The new, enhanced edition of a standard text includes substantial new content and new pedagogical features. Coverage begins with a chapter on financial accounting and accounting standards and continues through 24 chapters covering such topics as income statement and related information; balance sheet and statement of cash flows; valuation of inventories; depreciations, impairments, and depletion; intangible assets; long-term liabilities; income taxes; and pensions and postretirement benefits; among other topics. Annotation c. by Book News, Inc., Portland, Or.

Interesting textbook: Irish Spirit or Whiskey and Spirits For Dummies

Markov Decision Processes: Discrete Stochastic Dynamic Programming

Author: Martin L Puterman

The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists.
"This text is unique in bringing together so many results hitherto found only in part in other texts and papers. . . . The text is fairly self-contained, inclusive of some basic mathematical results needed, and provides a rich diet of examples, applications, and exercises. The bibliographical material at the end of each chapter is excellent, not only from a historical perspective, but because it is valuable for researchers in acquiring a good perspective of the MDP research potential."
-Zentralblatt fur Mathematik
". . . it is of great value to advanced-level students, researchers, and professional practitioners of this field to have now a complete volume (with more than 600 pages) devoted to this topic. . . . Markov Decision Processes: Discrete Stochastic Dynamic Programming represents an up-to-date, unified, and rigorous treatment of theoretical and computational aspects of discrete-time Markov decision processes."
-Journal of the American Statistical Association

Booknews

An up-to-date, unified, and rigorous treatment of the theoretical, computational, and applied research on Markov decision process models. It discusses all major research directions in the field, highlights many significant applications, and explores a number of important topics that have either been ignored or given short shrift in the literature. The focus is primarily on infinite horizon discrete time models and models with discrete time spaces, but models with arbitrary state spaces, finite horizon models, and continuous-time discrete state models are also examined. Annotation c. Book News, Inc., Portland, OR (booknews.com)

Table of Contents:

1	Introduction	1
2	Model formulation	17
3	Examples	33
4	Finite-horizon Markov decision processes	74
5	Infinite-horizon models : foundations	119
6	Discounted Markov decision problems	142
7	The expected total-reward criterion	277
8	Average reward and related criteria	331
9	The average reward criterion-multichain and communicating models	441
10	Sensitive discount optimality	492
11	Continuous-time models	530
App. A	Markov chains	587
App. B	Semicontinuous functions	602
App. C	Normed linear spaces	605
App. D	Linear programming	610