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Introduction to Probability and Stochastic Processes with Applications by Selvamuthu Dharmaraja, Viswanathan Arunachalam, Liliana Blanco Castaneda

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CHAPTER 1

BASIC CONCEPTS

During the early development of probability theory, the evolution was based more on intuition rather than mathematical axioms. The axiomatic basis for probability theory was provided by A. N. Kolmogorov in 1933 and his approach conserved the theoretical ideas of all other approaches. This chapter is based on the axiomatic approach and starts with this notion.

1.1   PROBABILITY SPACE

In this section we develop the notion of probability measure and present its basic properties.

When an ordinary die is rolled once, the outcome cannot be accurately predicted; we know, however, that the set of all possible outcomes is {1,2,3,4,5,6}. An experiment like this is called a random experiment.

Definition 1.1 (Random Experiment) An experiment is said to be random if its result cannot be determined beforehand.

It is assumed that the set of possible results of a random experiment is known. This set is called a sample space.

Definition 1.2 (Sample Space) The set Ω of all possible results of a random experiment is called a sample space. An element Image Image Ω is called an outcome or a sample point.

Image EXAMPLE 1.1

Experiment: Flipping a fair coin. The possible results in this case are ...

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