Books in Probability theory and stochastic processes

Identifiability In Stochastic Models

1st Edition
September 18, 2012
Bozzano G Luisa
English
eBook
9 7 8 0 1 2 8 0 1 5 2 6 1

The problem of identifiability is basic to all statistical methods and data analysis, occurring in such diverse areas as Reliability Theory, Survival Analysis, and Econometrics, where stochastic modeling is widely used. Mathematics dealing with identifiability per se is closely related to the so-called branch of "characterization problems" in Probability Theory. This book brings together relevant material on identifiability as it occurs in these diverse fields.

Probability and Random Processes

2nd Edition
January 11, 2012
Scott Miller + 1 more
English
Hardback
9 7 8 0 1 2 3 8 6 9 8 1 4
eBook
9 7 8 0 1 2 3 8 7 0 1 3 1

Probability and Random Processes, Second Edition presents pertinent applications to signal processing and communications, two areas of key interest to students and professionals in today's booming communications industry. The book includes unique chapters on narrowband random processes and simulation techniques. It also describes applications in digital communications, information theory, coding theory, image processing, speech analysis, synthesis and recognition, and others. Exceptional exposition and numerous worked out problems make this book extremely readable and accessible. The authors connect the applications discussed in class to the textbook. The new edition contains more real world signal processing and communications applications. It introduces the reader to the basics of probability theory and explores topics ranging from random variables, distributions and density functions to operations on a single random variable. There are also discussions on pairs of random variables; multiple random variables; random sequences and series; random processes in linear systems; Markov processes; and power spectral density. This book is intended for practicing engineers and students in graduate-level courses in the topic.

Stochastic Control by Functional Analysis Methods

1st Edition
Volume 11
August 18, 2011
A. Bensoussan
English
eBook
9 7 8 0 0 8 0 8 7 5 3 2 3

Applications of Variational Inequalities in Stochastic Control

1st Edition
Volume 12
August 18, 2011
A. Bensoussan + 1 more
English
eBook
9 7 8 0 0 8 0 8 7 5 3 3 0

Probabilities and Potential, B

1st Edition
Volume 72
August 18, 2011
C. Dellacherie + 1 more
English
eBook
9 7 8 0 0 8 0 8 7 1 8 3 7

An Introduction to Stochastic Modeling

4th Edition
November 18, 2010
Mark Pinsky + 1 more
English
Hardback
9 7 8 0 1 2 3 8 1 4 1 6 6
eBook
9 7 8 0 1 2 3 8 1 4 1 7 3

Serving as the foundation for a one-semester course in stochastic processes for students familiar with elementary probability theory and calculus, Introduction to Stochastic Modeling, Fourth Edition, bridges the gap between basic probability and an intermediate level course in stochastic processes. The objectives of the text are to introduce students to the standard concepts and methods of stochastic modeling, to illustrate the rich diversity of applications of stochastic processes in the applied sciences, and to provide exercises in the application of simple stochastic analysis to realistic problems. New to this edition: Realistic applications from a variety of disciplines integrated throughout the text, including more biological applications Plentiful, completely updated problems Completely updated and reorganized end-of-chapter exercise sets, 250 exercises with answers New chapters of stochastic differential equations and Brownian motion and related processes Additional sections on Martingale and Poisson process

Essential Bayesian Models

1st Edition
November 17, 2010
C.R. Rao + 1 more
English
Hardback
9 7 8 0 4 4 4 5 3 7 3 2 4

This accessible reference includes selected contributions from Bayesian Thinking - Modeling and Computation, Volume 25 in the Handbook of Statistics Series, with a focus on key methodologies and applications for Bayesian models and computation. It describes parametric and nonparametric Bayesian methods for modeling, and how to use modern computational methods to summarize inferences using simulation. The book covers a wide range of topics including objective and subjective Bayesian inferences, with a variety of applications in modeling categorical, survival, spatial, spatiotemporal, Epidemiological, small area and micro array data.

Introduction to Probability Models, Student Solutions Manual (e-only)

1st Edition
January 1, 2010
Sheldon M. Ross
English
eBook
9 7 8 0 1 2 3 8 1 4 3 6 4

Markov Processes for Stochastic Modeling

1st Edition
September 2, 2008
Oliver Ibe
English
eBook
9 7 8 0 0 8 0 9 2 2 4 5 4

Markov processes are used to model systems with limited memory. They are used in many areas including communications systems, transportation networks, image segmentation and analysis, biological systems and DNA sequence analysis, random atomic motion and diffusion in physics, social mobility, population studies, epidemiology, animal and insect migration, queueing systems, resource management, dams, financial engineering, actuarial science, and decision systems. This book, which is written for upper level undergraduate and graduate students, and researchers, presents a unified presentation of Markov processes. In addition to traditional topics such as Markovian queueing system, the book discusses such topics as continuous-time random walk,correlated random walk, Brownian motion, diffusion processes, hidden Markov models, Markov random fields, Markov point processes and Markov chain Monte Carlo. Continuous-time random walk is currently used in econophysics to model the financial market, which has traditionally been modelled as a Brownian motion. Correlated random walk is popularly used in ecological studies to model animal and insect movement. Hidden Markov models are used in speech analysis and DNA sequence analysis while Markov random fields and Markov point processes are used in image analysis. Thus, the book is designed to have a very broad appeal.

Introduction to Probability Models

10th Edition
December 11, 2006
Sheldon M. Ross
English
eBook
9 7 8 0 1 2 3 7 5 6 8 7 9

Introduction to Probability Models, Tenth Edition, provides an introduction to elementary probability theory and stochastic processes. There are two approaches to the study of probability theory. One is heuristic and nonrigorous, and attempts to develop in students an intuitive feel for the subject that enables him or her to think probabilistically. The other approach attempts a rigorous development of probability by using the tools of measure theory. The first approach is employed in this text. The book begins by introducing basic concepts of probability theory, such as the random variable, conditional probability, and conditional expectation. This is followed by discussions of stochastic processes, including Markov chains and Poison processes. The remaining chapters cover queuing, reliability theory, Brownian motion, and simulation. Many examples are worked out throughout the text, along with exercises to be solved by students. This book will be particularly useful to those interested in learning how probability theory can be applied to the study of phenomena in fields such as engineering, computer science, management science, the physical and social sciences, and operations research. Ideally, this text would be used in a one-year course in probability models, or a one-semester course in introductory probability theory or a course in elementary stochastic processes.

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