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Introduction to Probability, Models and Applications von Balakrishnan, N. (eBook)

  • Erscheinungsdatum: 17.04.2019
  • Verlag: Wiley
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Introduction to Probability,

An essential guide to the concepts of probability theory that puts the focus on models and applications Introduction to Probability offers an authoritative text that presents the main ideas and concepts, as well as the theoretical background, models, and applications of probability. The authors-noted experts in the field-include a review of problems where probabilistic models naturally arise, discuss the appropriate statistical methods, and explain how these models fit into the data presented. To aid in understanding, the book presents many real-world exercises and solutions that appear after each section within a chapter. A wide-range of topics are covered that include concepts of probability, univariate discrete distribution, univariate continuous distributions, bivariate discrete random variables, bivariate continuous random variables, stochastic independence-multivariate random variables, and many more. Designed as a useful guide, the text contains theory of probability, definitions, charts, examples, illustrations, problems and solutions, and a glossary. This important text: Includes classroom-tested problems and solutions to probability exercises Highlights real-world exercises designed to make clear the concepts presented Uses Matlab software to illustrate the text's computer exercises Features applications representing worldwide situations and processes Offers a Student Solutions Manual that contains select solutions to numerous exercises found in the book
Written for students majoring in statistics, engineering, operations research, computer science, physics, and mathematics, Introduction to Probability: Models and Applications is an accessible text that explores the basic concepts of probability and includes detailed information on models and applications. N. Balakrishnan, PhD, is a Distinguished University Professor in the Department of Mathematics and Statistics at McMaster University in Ontario, Canada. He is the author of over twenty Wiley books and served as co-editor of the Wiley's Encyclopedia of Statistical Sciences, Second Edition. Markos V. Koutras, PhD, is Professor in the Department of Statistics and Insurance Science at the University of Piraeus, Greece. Konstadinos G. Politis, PhD, is Associate Professor in the Department of Statistics and Insurance Science at the University of Piraeus, Greece.

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Introduction to Probability,

1
The Concept of Probability

Andrey Nikolaevich Kolmogorov (Tambov, Russia 1903-Moscow 1987)
Source : Keystone-France / Getty Images.

Regarded as the founder of modern probability theory, Kolmogorov was a Soviet mathematician whose work was also influential in several other scientific areas, notably in topology, constructive logic, classical mechanics, mathematical ecology, and algorithmic information theory.

He earned his Doctor of Philosophy (PhD) degree from Moscow State University in 1929, and two years later, he was appointed a professor in that university. In his book, Foundations of the Theory of Probability , which was published in 1933 and which remains a classic text to this day, he built up probability theory from fundamental axioms in a rigorous manner, comparable to Euclid's axiomatic development of geometry.
1.1 Chance Experiments - Sample Spaces

In this chapter, we present the main ideas and the theoretical background to understand what probability is and provide some illustrations of the way it is used to tackle problems in everyday life. It is rather difficult to try to answer the question "what is probability?" in a single sentence. However, from our experience and the use of this word in common language, we understand that it is a way to deal with uncertainty in our lives. In fact, probability theory has been referred to as "the science of uncertainty"; although intuitively most people associate probability with the degree of belief that something may happen, probability theory goes far beyond that as it attempts to formalize uncertainty in a way that is universally accepted and is also subject to rigorous mathematical treatment.

Since the idea of uncertainty is paramount when we discuss probability, we shall first introduce a concept that is broad enough to deal with uncertainties in a wide-ranging context when we consider practical applications. A chance experiment or a random experiment is any process which leads to an outcome that is not known beforehand. So tossing a coin, selecting a person at random and asking their age, or testing the lifetime of a new machine are all examples of random experiments.
Definition 1.1

A sample space of a chance experiment is the set of all possible outcomes that may appear in a realization of this experiment. The elements of are called sample points for this experiment. A subset of is called an event .

An event , consisting of a single sample point, i.e. a single outcome , is called an elementary event . We use capital letters , and so on to denote events. 1 If an event consists of more than one outcome, then it is called a compound event .

The following simple examples illustrate the above concepts.
Example 1.1

Perhaps the simplest example of a chance experiment is tossing a coin. There are two possible outcomes - Heads (denoted by and Tails (denoted by . In this notation, the sample space of the experiment is

If we toss two coins instead, there are four possible outcomes, represented by the pairs . The sample space for this experiment is thus

Here, the symbol means that both coins land Heads, while means that the first coin lands Heads and the second lands Tails. Note in particular that we treat the two events and as distinguishable, rather than combining them into a single event. The main reason for this is that the events and are elementary events, while the event "one coin lands Heads and the other lands Tails," which contains both and , is no longer an elementary event. As we will see later on, when we assign probabilities to the events of a sample space, it is much easier to work with elementary events, since in many cases such events are equally likely, and so it is reasonable the same probability to be assigned to each of them.

Consid

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