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Tch-prob1 Chapter 2 Concepts of Prob. Theory Random Experiment: an experiment in which outcome varies in an unpredictable fashion when the experiment is.

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Presentation on theme: "Tch-prob1 Chapter 2 Concepts of Prob. Theory Random Experiment: an experiment in which outcome varies in an unpredictable fashion when the experiment is."— Presentation transcript:

1 tch-prob1 Chapter 2 Concepts of Prob. Theory Random Experiment: an experiment in which outcome varies in an unpredictable fashion when the experiment is repeated under the same condition Specified by: 1. An experimental procedure 2. One or more measurements or observations

2 tch-prob2 EXAMPLE 2.1 : Experiment E 1 : Select a ball form an urn containing balls numbered 1 to 50. Note the number of the ball. Experiment E 2 : Select a ball form an urn containing balls numbered 1 to 4. Suppose that balls 1 and 2 are black and that balls 3 and 4 are white. Note number and color of the ball you select. Experiment E 3 : Toss a coin three times and note the sequence of heads/ tails. Experiment E 4 : Toss a coin three times and note the number of heads. Experiment E 6 : A block of information is transmitted repeatedly over a noisy channel until an error-free block arrives at the receiver. Experiment E 7 : Pick a number at random between zero and one. Experiment E 12 : Pick two numbers at random between zero and one. Experiment E 13 : Pick a number X at random between zero and one, then pick a number Y at random between zero and X.

3 tch-prob3 Discussions Compare E3 and E4: same procedure, different observations A random experiment with multiple measurements or observations: E2, E3, E12, E13 sequential experiment consists of multiple subexperiments: E3, E4, E6, E12, E13 dependent subexperiments: E13

4 tch-prob4 Sample Space Outcome, sample point: –one and only one per experiment –mutually exclusive, cannot occur simultaneously Sample space: set of all possible outcomes Example 2.2: sample spaces number of outcomes: –Finite –countably infinite S6 –unaccountably infinite S7 Continuous Sample space Discrete sample space

5 tch-prob5 EXAMPLE 2.2 : The sample spaces corresponding to the experiments in Example 2.1 are given below using set notation :

6 tch-prob6 Event: a subset of S, a collection of outcomes that satisfy certain conditions Certain event: S null event: elementary event: event of a single outcome Example 2.3: A2: The ball is white and even-numbered A4: The number of heads equals the number of tails A7: The number selected is nonnegative

7 tch-prob7 Set Operations 1.Union 2.Intersection mutually exclusive 3.Complement imply equal A=B - Commutative properties of set operations - Associative properties of set operations - Distributive properties of set operations - De Morgan’s Rules Venn diagrams: Fig. 2.2 A B

8 tch-prob8 Example 2.5: Configuration of a three-component system a. series all three b. parallel at least one of three c. two-out-of-three C1C2C3 C1 C2 C3

9 tch-prob9 2.2 The Axioms of Probability A probability law for the experiment E is a rule that assigns to each event a number P(A), called the probability of A, that satisfies the following axioms: Axiom I: nonnegative Axiom II: P(S)=1 total=1 Axiom III: If, then Axiom III’: If for all, then A set of consistency rules that any valid probability assignment must satisfy.

10 tch-prob10 Corollary 1. pf: Corollary 2. pf: from Cor.1, Corollary 3. pf: Let A=S, in Cor.1. Corollary 4. are pairwise mutually exclusive, then for

11 tch-prob11 Corollary 5. pf: Corollary 6. Corollary 7. If, then pf.

12 tch-prob12 Axioms + corollaries  provide rules for computing the probability of certain events in terms of other events. However, we still need initial probability assignment 1.Discrete Sample Spaces find the prob.of elementary events; all distinct elementary events are mutually exclusive, Example 2.6., 2.7 2. Continuous Sample Spaces assign prob. to intervals of the real line or rectangular regions in the plane Example 2.11

13 tch-prob13 Discrete Sample Spaces First, suppose that the sample space is finite, and is given by If S is countably infinite, then Axiom Ⅲ ’ implies that the probability of an event such as is given by (by corollary ?)

14 tch-prob14 If the sample space has n elements,, a probability assignment of particular interest is the case of equally likely outcomes. The probability of the elementary events is The probability of any event that consists of k outcomes, say, is Thus, if outcomes are equally likely, then the probability of an event is equal to the number of outcomes in the event divided by the total number of outcomes in the sample space. (remember the classical definition?)

15 tch-prob15 EXAMPLE 2.6 An urn contains 10 identical balls numbered 0,1,…, 9. A random experiment involves selecting a ball from the urn and noting the number of the ball. Find the probability of the following events : A = “number of ball selected is odd,” B = “number of ball selected is a multiple of 3,” C = “number of ball selected is less than 5,” and of and.

16 tch-prob16 The sample space is, so the sets of outcomes corresponding to the above events are,, and. If we assume that the outcomes are equally likely, then. From Corollary 5..

17 tch-prob17 where we have used the fact that, so. From Corollary 6,

18 tch-prob18 EXAMPLE 2.7 Suppose that a coin is tossed three times. If we observe the sequence of heads and tails, then there are eight possible outcomes. If we assume that the outcomes of S 3 are equiprobable, then the probability of each of the eight elementary events is 1/8. This probability assignment implies that the probability of obtaining two heads in three tosses is, by Corollary 3, * If we count the number of heads in three tosses, then

19 tch-prob19 Continuous Sample Spaces EXAMPLE 2.11 Consider Experiment E 12, where we picked two number x and y at random between zero and one. The sample space is then the unit square shown in Fig. 2.8(a). If we suppose that all pairs of numbers in the unit square are equally likely to be selected, then it is reasonable to use a probability assignment in which the probability of any region R inside the unit square is equal to the area of R. Find the probability of the following events:,, and. Figures 2.8(b) through 2.8(c) show the regions corresponding to the events A, B, and C. Clearly each of these regions has area ½. Thus

20 tch-prob20 FIGURE 2.8 a Two-dimensional sample space and three events. (a) Sample space x y 0 1 S y x (b) Event 0 1 x y (c) Event 0 1 x y (d) Event 0 1 x y

21 tch-prob21 2.3 Computing probabilities using counting methods In many experiments with finite sample space, the outcomes can be assumed to be equiprobable. Prob. Counting the number of outcomes in an event 1.Sampling with Replacement and with Ordering choose k objects from a set A that has n distinct objects, with replacement and note the order. The experiment produces an ordered k-tuple where and The number of distinct ordered k-tuples = Ex.2.12 5 balls, select 2 Pr[2 balls with same number] =

22 tch-prob22 2.Sampling without Replacement and with ordering choose k objects from a set A that has n distinct objects, without replacement. number of distinct ordered k-tuples = n (n-1)…(n-k+1) Ex.2.13 5 balls, select 2 Pr [1st number >2nd number ]= 10/20

23 tch-prob23 Permutations of n Distinct Objects Consider the case when k=n number of permutations of n objects = n! For large n, stirling’s formula is useful Ex. 2.16 12 balls randomly placed into 12 cells, where more than 1 ball is allowed to occupy a cell. Pr [ all cells are occupied ]= ~

24 tch-prob24 3.Sampling without Replacement and without ordering ~ ” combination of size k” = each combination has k! possible orders “binomial coefficient”, read “n choose k” Ex. 2.18 The number of distinctive permutation of k white balls and n-k black balls

25 tch-prob25 Ex. 2.19 A batch of 50 items contains 10 defective items. Select 10 items at random, Pr [ 5 out of 10 defective ]=? Exercise prob. 47: Multinomial Ex.2.20 Toss a die 12 times. Pr [ each number appears twice ]= ? {1,1,2,2,3,3, …….6,6} permutation

26 tch-prob26 4. Sampling with Replacement and without ordering Suppose k=5, n=4 object 1 2 3 4 XX X XX The result can be summarized as XX // X / XX = k: x n-1: / = Ex. Place k balls into n cells. Pick k balls from an urn of n balls. (k may be greater than n)

27 tch-prob27 2.4 Conditional Probability We are interested in knowing whether A and B are related, i.e., if B occurs, does it alter the likelihood of A occurs. Define the conditional probability as for P[B]>0 - renormalize

28 tch-prob28 Example 2.21. A ball is selected from an urn containing 2 black balls (1,2) and 2 white balls (3,4). A: Black ball selected ½ B: even-numbered ball ½ C: number > 2 ½ Not related Related

29 tch-prob29 is useful in finding prob. in sequential experiments Ex.2.22 An urn contains 2 black, 3 white balls. Two balls are selected without replacement. Sequence of colors is noted. Sol. 1. b b two subexperiments b w w b w w Tree diagram

30 tch-prob30 Let be mutually exclusive, whose union = S, partition of S. “ Theorem on total probability” Ex. 2.24 In prev. example (2.22) find P[W 2 ]=Pr [second ball is white] A S B1 B2 Bn

31 tch-prob31 Suppose event A occurs, what is the prob. of event Bj? Bayes’ Rule Before experiment: P[ Bj ]= a priori probability After experiment: A occurs P[ Bj/A ]= a posteriori probability

32 tch-prob32 Ex.2.26. Binary symmetric channel Suppose

33 tch-prob33 2.5 Independence of Events If knowledge of the occurrence of an event B does not alter the probability of some other event A, then A is independent of B. Two events A and B are independent if

34 tch-prob34 Ex. 2.28. A ball is selected from an urn of 4 balls {1,b},{2,b},{3,w},{4,w} A: black ball is selected B: even-numbered ball C: number >2 P[A]=P[B]=P[C]=0.5 If two events have non zero prob., and are mutually exclusive, then they cannot be independent.

35 tch-prob35 Three events A, B, and C are independent if Ex. 2.30. Common application of the independence concept is in making the assumption that the event of separate experiments are independent. Ex.2.31. A fair coin is tossed three times. P[HHH]=1/8, ….

36 tch-prob36 2.6. Sequential Experiments Sequence of independent experiments If sub experiments are independent, and event concerns outcomes of the kth subexperiment, then are independent. Bernoulli trial: Perform an experiment once, note whether an event A occurs. success, if A occursprob= p failure, otherwise. prob= 1-p Pn(k)= prob. of k successes in n independent repetitions of a Bernoulli trial = ?

37 tch-prob37 Ex.2.34. toss a coin 3 times. P[ H ]= p Binomial probability law k=0,1,…,n

38 tch-prob38 Binomial theorem Let a=b=1, Let Ex.2.37. A binary channel with BER. 3 bits are transmitted. majority decoding is used. What is prob. of error?

39 tch-prob39 Multinomial Probability Law Bj’s: partition of S; mutually exclusive n independent repetitions of experiment number of times Bj occurs. Ex.2.38. dart 3 areas throw dart 9 times, P[3 on each area]=?

40 tch-prob40 Geometric Probability Law Repeat independent Bernoulli trials until the occurrence of the first success. P[ more than k trials are required before a success occurs ] P[The initial k trials are failures]

41 tch-prob41 Sequence of Dependent Experiments Ex.2.41. A sequential experiment involves repeatedly drawing a ball from one of two urns, noting the number on the ball, and replacing the ball in its urn. Urn 0 : 1 ball #1, 2 balls #0 Urn 1 : 5 balls #1, 1 ball #0 Initially, flip a coin. Thereafter, the urn used in the subexperiment corresponds to the number selected in the previous subexperiment. Trellis diagram Urn 2 Urn 1

42 tch-prob42 conditional prob. Markov property Ex.2.42 calculate with

43 tch-prob43 Problem 95 on Page 83 95Consider a well-shuffled deck of cards consisting of 52 distinct cards, of which four are aces and four are kings. a. Pr[Obtaining an ace in the first draw]= b. Draw a card from the deck and look at it. Pr[Obtaining an ace in the second draw]= If does not know the previous outcome

44 tch-prob44 c. Suppose we draw 7 cards from the deck. Pr[7 cards include 3 aces]= Pr[7 cards include 2 kings]= Pr[7 cards includes 3 aces and 2 kings]= d. Suppose the entire deck of cards is distributed equally among four players. Pr[each player gets an ace]=

45 tch-prob45 Birthday problem. p =No two people in a group of n people will have a common birthday. Lotto Problem. Six numbers and one special number are picked from a pool of 42 numbers. … 1.What is the probability of winning the grand prize? 2.What is the probability of winning 2 nd prize? 3.What is the probability that number 37 is drawn next time?

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