Introduction to Probability & Stochastic Processes with Applications Reviewed By Manoj Rengarajan of Bookpleasures.com

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Manoj Rengarajan

**Reviewer Manoj
Rengarajan **holds a Master of Financial Engineering - University of
California, Berkeley and he works in the investment management
industry and specializes in providing economic and investment outlook
and strategy for global equity and government bond markets. He has an
educational background in financial engineering, business, and
engineering, and professional interests include business,
finance, economics, technology and related areas.

By Manoj Rengarajan

Published on July 13, 2012

Authors: Liliana Blaco Castaneda, Viswanathan Arunachalam, Selvamuthu Dharmaraja

Publisher: Wiley

ISBN:
978-1118294406

Authors: Liliana Blaco Castaneda, Viswanathan Arunachalam, Selvamuthu Dharmaraja

Publisher: Wiley

Follow Here To Purchase Introduction to Probability and Stochastic Processes with Applications

Authors: Liliana Blaco Castaneda,
Viswanathan Arunachalam, Selvamuthu Dharmaraja

Publisher: Wiley

ISBN:
978-1118294406

Modeling of random processes is essential in
several fields including manufacturing, bio sciences, and business
to name a few and a solid understanding of probability theory forms
the basis of modeling random processes. The book aims to provide a
solid but easy to understand treatment of the subject for a
beginner.

The introduction gives a brief overview of the
subject as well as the history and provides the motivation behind
studying probability theory.

Basic concepts starts with
definitions for probability space and theorems, conditional
probability and event independence. In this way, the book does not
assume any background in the subject. The general math
pre-requisites such as linear algebra, set theory, and
combinatorics are covered briefly in the appendix.

The
chapter on random variables and distributions describe a whole
ranges of both discrete and continuous random variables including
basic definition and their properties.

Within discrete
distributions, the book covers the binomial, hypergeometric,
poisson and geometric distributions and within continuous
distributions, the book covers normal, gamma and beta
distributions.

In addition to just describing the general
properties of the distribution the part on relationships between
distributions gives a great overview to see how the different
distributions are connected. The examples and exercises cover a
whole range of applications.

The joint distribution of
random variables is then covered followed by a really useful
concept of probability theory - conditional expected values.The
section on multivariate normal distribution and properties includes
standard basic treatment of the means, variances and
covariances.

Limit theorems cover the theory about how to
draw conclusions about populations from a sample and to quantify
how reliable the conclusions are. The chapter goes on to the weak
and strong laws of large numbers, markov and chebyshev
inequalities, and chebyshev’s theorem - one of the most important
results in probability theory.

The final chapters are a
quick introduction to concepts which are targeted towards students
of math finance. The introduction to stochastic processes covers
basics such as stationary processes, independent and stationary
increments, markov chains and poisson processes.

Queueing
models deal with basic markovian single and multi server models as
well as a brief discussion on non markov models, while the chapter
on stochastic calculus deals with martingales and brownian
motion.

Throughout the book there are a variety of examples
to link theory to practise. The exercise list at the end of each
chapter is exhaustive and provides adequate drill in helping to
understand concepts.

The book clearly meets its goal of
providing a clear and solid introduction to probability theory and
stochastic processes. The simple style and orientation towards
putting concepts to practise make this as a good book for a
practitioner.

Follow Here To Purchase Introduction to Probability and Stochastic Processes with Applications