## Ebooks

An Introduction to Stochastic Modeling, Student Solutions Manual (e-only)

New to this edition:

Realistic applications from a variety of disciplines integrated throughout the text, including more biological applicationsPlentiful, completely updated problemsCompletely updated and reorganized end-of-chapter exercise sets, 250 exercises with answersNew chapters of stochastic differential equations and Brownian motion and related processesAdditional sections on Martingale and Poisson processRealistic applications from a variety of disciplines integrated throughout the textExtensive end of chapter exercises sets, 250 with answersChapter 1-9 of the new edition are identical to the previous editionNew! Chapter 10 - Random EvolutionsNew! Chapter 11- Characteristic functions and Their ApplicationsThe publication first takes a look at the elements of stochastic processes, Markov chains, and the basic limit theorem of Markov chains and applications. Discussions focus on criteria for recurrence, absorption probabilities, discrete renewal equation, classification of states of a Markov chain, and review of basic terminologies and properties of random variables and distribution functions. The text then examines algebraic methods in Markov chains and ratio theorems of transition probabilities and applications.

The manuscript elaborates on the sums of independent random variables as a Markov chain, classical examples of continuous time Markov chains, and continuous time Markov chains. Topics include differentiability properties of transition probabilities, birth and death processes with absorbing states, general pure birth processes and Poisson processes, and recurrence properties of sums of independent random variables. The book then ponders on Brownian motion, compounding stochastic processes, and deterministic and stochastic genetic and ecological processes.

The publication is a valuable source of information for readers interested in stochastic processes.

Organized into nine chapters, this volume begins with an overview of the fundamental concepts in game theory, namely, strategy and pay-off. This text then examines the identification of strategies with points in Euclidean n-space, which is a convenience that simplifies the mathematical analysis. Other chapters provide a discussion of the theory of finite convex games. This book discusses as well the extension of the theory of convex continuous games to generalized convex games, which leads to the characterization that such games possess optimal strategies of finite type. The final chapter deals with the components of a simple two-person poker game.

This book is a valuable resource for mathematicians, statisticians, economists, social scientists, and research workers.

A First Course. We emphasize a careful treatment of basic structures in stochastic processes in symbiosis with the analysis of natural classes of stochastic processes arising from the biological, physical, and social sciences.

This volume is organized into three sections encompassing 31 chapters and begins by summarizing the results of field and laboratory research that investigated gene frequency patterns in space and time of animal and plant populations. This book then explains the origin of new taxa; animal and plant domestication; variation in heritability related to parental age; and problems in the genetics of certain haplo-diploid populations. The next section offers a combination of data analyses and interpretations of related models, with some papers devoted to the origin of race formation and the interaction between sexual selection and natural selection. Among the theoretical studies presented are facets of selection migration interaction; stochastic selection effects; properties of density and frequency dependent selection; concepts and measures of genetic distance and speciation; aspects of altruism; and kin selection.

This book will be of interest to naturalists, experimentalists, theoreticians, statisticians, and mathematicians.

Organized into nine chapters, this book begins with an overview of diverse types of stochastic models, which predicts a set of possible outcomes weighed by their likelihoods or probabilities. This text then provides exercises in the applications of simple stochastic analysis to appropriate problems. Other chapters consider the study of general functions of independent, identically distributed, nonnegative random variables representing the successive intervals between renewals. This book discusses as well the numerous examples of Markov branching processes that arise naturally in various scientific disciplines. The final chapter deals with queueing models, which aid the design process by predicting system performance.

This book is a valuable resource for students of engineering and management science. Engineers will also find this book useful.

The authors have made three main kinds of changes. First, they have enlarged on the topics treated in the first edition. Second, they have added many exercises and problems at the end of each chapter. Third, and most important, they have supplied, in new chapters, broad introductory discussions of several classes of stochastic processes not dealt with in the first edition, notably martingales, renewal and fluctuation phenomena associated with random sums, stationary stochastic processes, and diffusion theory.