Introduction To Probability Models is dedicated to introducing the users to the basics of probability and its related concepts.
Summary Of The Book
Probability refers to estimating the likelihood of the occurrence of a certain event or of a statement being true. The probability of any event lies between 0 and 1. The closer the figure is to 1, the more likely the event shall occur. Introduction To Probability Models contains chapters on the basics and the details of probability.
The book was published in 2013 by Elsevier India. It is good for those wanting to use probability in the study of other fields like management science, engineering, physical science, operations research and social science. It has extensively been made use of by professors and as a text for undergraduate courses in applied probability. It explains stochastic processes, elementary probability theory and the use of probability theory in several other fields like computer science, engineering, social science, operations research and mathematics. The text has also been recommended by the Society of Actuaries after the addition of some new chapters on actuaries.
The problems in the book have been presented in a manner that requires students to think extensively in order to be able to solve them. The book has been divided into eleven parts. The first part is an introduction to probability theory containing information on topics like sample space, events, independent events, conditional probability and Bayes’ Formula. The second part deals with Random variables and contains topics like discrete random variables, continuous random variables, expectations of random variables, distributed random variables, and moment generating functions.
The third part is on conditional probabilities and has information on computing of expectations through conditioning, computation of probabilities through conditioning, and compound random variables. Another part on Continuous Time Markov Chains contains topics like birth and death processes, time reversibilities, limiting probabilities, and uniformization. Another part on queuing theory deals with steady state probabilities, cost equation, and exponential models.
The last part on Simulation is about techniques for simulation of continuous random variables, inverse transformation method, hazard rate method, rejection method, gamma distribution, normal distribution, and control variables.
About Ross S. M.
Sheldon M. Ross is a professor of Industrial and Systems Engineering at the University Of South California and the author of several books.
Other books by the author are Introduction to Mathematical Finance: Options and Other Topics, Simulation, A First Course in Probability, and Probability Models for Computer Science.
The author did his Bachelor’s in Science (Mathematics) in 1963 from Brooklyn College and his Master’s in Science in Mathematics from Purdue University. He later obtained his Doctorate in Statistics from Stanford University. He worked as a professor at the University of California for several years and also as an editor for several journals including Probability in the Engineering and Informational Sciences, the Journal of Bond Trading and Management, and the International Journal of Quality Technology and Quantitative Management.
