Probability and Statistical Inference. Robert Bartoszynski
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Название: Probability and Statistical Inference

Автор: Robert Bartoszynski

Издательство: John Wiley & Sons Limited

Жанр: Математика

Серия:

isbn: 9781119243823

isbn:

СКАЧАТЬ to a random choice according to the other two schemes.

      To see why it is so, we will show that the first and second scheme are not equivalent. The analogous arguments for the other two possible pairs of schemes are left as an exercise.

Geometry explaining Bertrand's paradox, depicting that the angle AOB is a, and that a device chooses angles a at random to produce more intersections of the diameter that are farther from the center.

      Example 2.3

      Problems

      1 2.3.1 Label all statements below as true or false. (i) If is more likely to occur than , then . (ii) If occurs whenever does, then . (iii) If then whenever occurs, does also. (iv) If , then must occur three times out of every four. (v) The sum of probabilities of disjoint events and cannot exceed 1. (vi) If and are not disjoint, the sum of their probabilities exceeds 1. (vii) If and are all positive, then is also positive. (viii) If sample spaces for two experiments are identical, then the probability of the same event must be the same for both experiments.

      2 2.3.2 A bathroom floor is covered by square tiles with side length . You drop a coin with diameter , where . Find: (i) The probability that the coin will rest entirely within one tile. (ii) The probability that the coin will partially cover four different tiles.

      3 2.3.3 Show that first and third, as well as second and third, schemes of sampling chords (see Bertrand's paradox) are not equivalent.

      

      The simplest consequences of the axioms of probability are as follows:

      1 The probability of the impossible event is zero:(2.1) This follows from the fact which is possible only if . It is important to realize that the converse is not true: the condition does not imply that . This is shown by the following example:Example 2.4Consider an experiment consisting of tossing a coin infinitely many times. The outcomes may be represented as infinite sequences of the form HHTHTTHT …so that the sample space contains infinitely many of such sequences. The event “heads only,” that is, the set consisting of just one sequence HHHH …, is not empty. However, the chance of such an outcome is, at least intuitively, zero: tails should come up sooner or later.

      2 Probability is finitely additive:for any if the events are pairwise disjoint.In an infinite sequence events are pairwise disjoint only if are, so Axiom 3 applies. Therefore, using (2.1), we havewhile the left‐hand side is

      3 Monotonicity: If then . This follows from the fact that . The events on the right‐hand side are disjoint, so we have by Axiom 1. Since for every event , we have

      4 Probability is countably subadditive:(2.2) for every sequence of events This follows from representation (1.6) as a union of disjoint events, and then from monotonicity. We have

      5 Complementation:(2.3) This follows from Axiom 2, by the fact that and are disjoint and .

      6 Probability of a union of events:(2.4) Indeed, since , then . On the other hand, , and hence, . Solving for in one equation and substituting into the other, we obtain (2.4).

A Venn diagram depicting the union of two events A and B. Each sample point from the intersection A ∩ B is included twice, to obtain the probability of the union A ∪ B.

      Example 2.5

      Suppose that images. Find images.

      Solution