Robert P. Dobrow - Probability

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Discover the latest edition of a practical introduction to the theory of probability, complete with R code samples In the newly revised Second Edition of
distinguished researchers Drs. Robert Dobrow and Amy Wagaman deliver a thorough introduction to the foundations of probability theory. The book includes a host of chapter exercises, examples in R with included code, and well-explained solutions. With new and improved discussions on reproducibility for random numbers and how to set seeds in R, and organizational changes, the new edition will be of use to anyone taking their first probability course within a mathematics, statistics, engineering, or data science program.
New exercises and supplemental materials support more engagement with R, and include new code samples to accompany examples in a variety of chapters and sections that didn’t include them in the first edition.
The new edition also includes for the first time: 
A thorough discussion of reproducibility in the context of generating random numbers Revised sections and exercises on conditioning, and a renewed description of specifying PMFs and PDFs Substantial organizational changes to improve the flow of the material Additional descriptions and supplemental examples to the bivariate sections to assist students with a limited understanding of calculus Perfect for upper-level undergraduate students in a first course on probability theory, is also ideal for researchers seeking to learn probability from the ground up or those self-studying probability for the purpose of taking advanced coursework or preparing for actuarial exams.

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In probability word problems, descriptive phrases are typically used rather than set notation. See Table 1.2for some equivalences.

A Venn diagram is a useful tool for working with events and subsets. A rectangular box denotes the sample space картинка 103, and circles are used to denote events. See Figure 1.1for examples of Venn diagrams for the most common combined events obtained from two events картинка 104and картинка 105.

One of the most basic, and important, properties of a probability function is the simple addition rule for mutually exclusive events. We say that two events are mutually exclusive , or disjoint , if they have no outcomes in common. That is, картинка 106and картинка 107are mutually exclusive if картинка 108, the empty set.

TABLE 1.2 . Events and sets.

Description Set notation
Either картинка 109or both occur картинка 110
картинка 111 картинка 112
Not картинка 113 картинка 114
картинка 115implies картинка 116; картинка 117is a subset of Probability - изображение 118 Probability - изображение 119
Probability - изображение 120 картинка 121
Neither картинка 122nor картинка 123 картинка 124
At least one of the two events occurs Probability - изображение 125
At most one of the two events occurs Probability - изображение 126
FIGURE 11 Venn diagrams ADDITION RULE FOR MUTUALLY EXCLUSIVE EVENTS If and - фото 127

FIGURE 1.1 : Venn diagrams.

ADDITION RULE FOR MUTUALLY EXCLUSIVE EVENTS

If and are mutually exclusive events then The addition rule is a consequ - фото 128and are mutually exclusive events then The addition rule is a consequence of the - фото 129are mutually exclusive events, then

The addition rule is a consequence of the third defining property of a - фото 130

The addition rule is a consequence of the third defining property of a probability function. We have that

where the third equality follows because the events are disjoint so no outcome - фото 131

where the third equality follows because the events are disjoint, so no outcome картинка 132will be counted twice. The addition rule for mutually exclusive events extends to more than two events.

EXTENSION OF ADDITION RULE FOR MUTUALLY EXCLUSIVE EVENTS

Suppose Probability - изображение 133is a sequence of pairwise mutually exclusive events. That is, картинка 134and are mutually exclusive for all Then Next we highlight other key pro - фото 135are mutually exclusive for all Then Next we highlight other key properties that are consequences of the - фото 136. Then

Next we highlight other key properties that are consequences of the defining - фото 137

Next, we highlight other key properties that are consequences of the defining properties of a probability function and the addition rule for disjoint events.

PROPERTIES OF PROBABILITIES

1 If implies , that is, if , then

2

3 For all events and , (1.3)

Each property is derived next.

1 As , write as the disjoint union of and . By the addition rule for disjoint events,because probabilities are nonnegative.

2 The sample space can be written as the disjoint union of any event and its complement . Thus,Rearranging gives the result.

3 Write as the disjoint union of and . Also write as the disjoint union of and . Then and thus,Observe that the addition rule for mutually exclusive events follows from Property 3 because if and are disjoint, then .

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