Discrete Math CS 280

1. Discrete MathCS 280 Prof. Bart Selman selman@cs.cornell.edu Module Probability --- Part b) Bayes’ Rule Random Variables

2. Bayes’ Theorem • How to assess the probability that a particular event will occur on the • basis of partial evidence? • Examples: • What is the likelihood that people who test positive to a particular disease • (e.g., HIV), actually have the disease? • What is the probability that an e-mail message is spam? • Key idea: one should factor in additional information regarding • occurrence of events.

3. Note: ¬F is also referred to as complement of F (FC or F). • Assume that with respect to events F and E (“E” for “Evidence”): • We know P(F) – probability that event F occurs • (e.g. probability that email message is spam; • this is given by what fraction of email is spam) • We also know event E has occurred. • (e.g., email message contains words “sale” and “bargain”) • Therefore the probability conditional probability that F occurs given • that E occurs, P(F|E), is a more realistic estimate that F occurs than P(F). • How do we compute P(F|E)? • E.g., based on P(F), P(E|F), and P(E| ¬F)

4. E Evidence Bayesian Inference Modified Belief Original Belief (Prior Probability) Hypothesis Theory P(F|E) P(F)

5. Box A Box B Experiment: Pick one box at random (p = 0.5) and than a ball at random from that box. Assume you got a red ball. What’s the probability that it came form the left box? Define: E – you choose a red ball. (therefore ¬ E – you choose the green ball) F – you choose the left box. (therefore ¬ F– you choose the right box) We want to know P(F|E)

6. E – red color F – left box P(F|E)? 7/9 • What we know: • P(E|F) = • P(E|¬F) = • Given that the boxes are selected at random: P(F) = P(¬F)=1/2 • P(F|E) = P(E∩F)/P(E)  so we need to compute P(E∩F) and P(E). 3/7 We know P(E|F) = P(E∩F)/P(F). So, P(E∩F) = P(E|F) P(F) = 7/9 * 1/2 = 7/18. What about P(E)? Note that P(E) = P(EF) +P (E∩ ¬F). Why? Note also that P (E ∩ ¬F)= P(¬F) P(E|¬F) = 1/2 * 3/7 = 3/14 So, P(E) = P(EF) +P (E∩ ¬F) = 7/18 + 3/14 = 38/63 And therefore P(F|E) = P(E∩F)/P(E) = (7/18) / (38/63) = 49/76 0.645

7. Original Belief there is a 0.5 that you will pick left box (P(F)). Concrete (new) Evidence Red ball picked (E) Bayesian Inference Modified Belief Increased to 0.65 Probability (P(F|E))

8. Why? Theorem: Bayes’ Theorem Suppose that E and F are events from a sample space S such that P(E) ≠0 and P(F)≠0. Then Proof:

9. Example: • Suppose that 1 person in 100,000 has a particular rare disease. There is an • accurate test for the disease that is correct in 99% of the time when given • to someone with the disease; it is correct in 99.5% of the time when given • to someone without the disease. • Find: • a) Probability that someone who tests positive has the disease. • b) Probability that someone who tests negative does not have the disease.

10. Always start by defining the events! F – the person has the disease E – the person tests positive to the disease P(F|E) – probability of having the disease given positive test P(F)=1/100,000 = 0.00001; P(FC) = 0.99999 P(E|F) = 0.99; P(EC|F) = 0.01 P(E|FC) = 0.005 • Solution: • a) Note: These are the probabilities most easily measured! Only 0.2% of people who test positive actually have the disease!!!

11. b) F – the person has the disease • E – the person tests positive to the disease • P(FC|EC) – probability of not having the disease given negative test • P(F)=1/100,000=0.00001; P(FC)=0.99999 • P(E|F)=0.99; P(EC|F)=0.01 • P(E|FC)=0.005 That’s… pretty good!

12. Marbles TOYS R US sells two kinds of bags of marbles: (1) Bags of all black marbles, and (2) Bags of mixed marbles in which 20% of the marbles are black. The bags are opaque and wrapped in plastic, and I have no idea which bag is more common. I buy a bag and figure there is a 50:50 chance that the bag I purchased contains all black marbles. A guess! I pull a marble out of the bag and see that it is black. How should this new evidence affect the 50:50 assessment I assigned to the probability of my having purchased an all black bag of marbles? (as previous example) F – bag of all black marbles; FC – bag with 20% black marbles E – black marble

13. Marbles Posterior Belief Probability that my bag of marbles is all black = 0.833 P(F|E). Prior Belief There is a 1/2 chance that I have an all-black bag of marbles … a guess (P(F)) 0.5 chance of all-black (100%) marble bag. 0.5 chance of 0.2 black marble bag.

14. Marbles Warning: Correct but slightly informal! Instead of changing prior, we could consider new experiment and evidence drawing two marbles. I put the marble back, shake the bag, and draw another marble. It is black? What happens now that my new prior probability is 0.83? Prior Belief 0.83 New Belief 0.96 0.17 chance of 0.2 black marble bag. 0.83 chance of all-black (1) marble bag. Remember, I don’t know which type of marble bag is most popular … Wal-Mart may have 100 bags of mixed marbles on the shelf for every bag of all black marbles. Bayes’ Theorem doesn’t tell me the probability of my marble bag being all black – it only tells me how I should revise my initial best guess based on the newly obtained information.

15. Original Belief I shrug my shoulders and guess is that there is a 0.5 chance that my bag contains all black marbles. Bayesian Inference Concrete Evidence 1st Black Marble Modified Belief Increased to 0.83 Probability Modified Belief Increased to 0.96 Bayesian Inference Concrete Evidence 2nd Black Marble

16. Generalized Bayes’ Theorem • Suppose that E is an event from a sample space S and F1, F2 ,…, Fn are • mutually exclusive events such that • Asume that P(E) ≠ 0 and P(Fi) ≠ 0 for i=1, 2,…, n. Then P(E) Compare:

17. Bayesian Spam Filters 17

18. Applying Bayes’ Theorem:SPAM or HAM? Let our sample space or universe be the set of emails. (So, we’re sampling from the space of possible emails.) Let S be the event a message is spam; hence is the event a message is not spam Let E be the event a message contains a word w. How do we get and ? Since we have no idea of likelihood of SPAM, we assume P(S)=P(SC)=1/2. Can we do better?

19. Estimations Note these are estimates based on frequencies in samples. 19

20. Estimation Continued So, becomes So, what do we want for p(w) and q(w) ?? So, a quite straightforward formula for our first Bayesian spam filter! Note P(S) = P(SC) = ½ divides out. 20

21. Spam based on single words? Probabilities based on single words: Bad Idea False positives AND false negatives a plenty Calculate based on n words, assuming each event Ei|S (Ei|SC) is independent; P(S) = P(SC). Derivation see Sect. 6.3. 21

22. Final Approximation Compare to single word: 22

23. How do we use this? User must train the filter based on messages in his/her inbox to estimate probabilities. The program or user must define a threshold probability r: If , the message is considered spam. Gmail: Train on all users! (note: report spam button) 23

24. Example Suppose the filter has the following data Threshold Probability: .9 “Nigeria” occurs in 250 of 2000 spam messages “Nigeria” occurs in only 5 of 1000 non-spam messages Let’s try to estimate the probability, using the process we just defined 24

25. Example Cont. Step 1: Find the probability that the message has the word “Nigeria” in it and is spam. p(Nigeria) = 250 / 2000 = 0.125 Step 2: Find the probability that the message has the word “Nigeria” in it and is not spam. q(Nigeria) = 5 / 1000 = 0.005 25

26. Since we are assuming that it is equally likely that an incoming message is or is not spam, we can estimate the probability with this equation: r(Nigeria) = p(Nigeria) p(Nigeria) + q(Nigeria) Example Cont. 26

27. = 0.125 0.130 = 0.962 Since r(Nigeria) is greater than the threshold of 0.9, we can reject this message as spam. Example Cont. 0.125____ 0.125 + 0.005 27

28. Multiple Words 2000 Spam messages; 1000 real messages “Nigeria” appears in 400 spam messages “Nigeria” appears in 60 real messages “bank” appears in 200 spam and 25 real messages Threshold Probability: .9 Let’s calculate the probability that message with “Nigeria” and “bank” is spam. 28

29. Example Cont. Step 1: Find the probability that the message has the word “Nigeria” in it and is spam. p(Nigeria) = 400 / 2000 = 0.2 Step 2: Find the probability that the message has the word “Nigeria” and is not spam. q(Nigeria) = 60 / 1000 = 0.06 Step 3: Find the probability that the message contains the word “bank” and is spam. p(bank) = 200 / 2000 = 0.1 Step 4: Find the probability that the message contains the word “bank” and is not spam. q(bank) = 25 / 1000 = 0.025 29

30. Example Cont Using our approximation, we have: r(Nigeria,bank) = p(Nigeria) * p(bank) p(Nigeria) * p(bank) + q(Nigeria) * q(bank) 30

31. Example Cont. Using our approximation, we have: r(Nigeria,bank) = p(Nigeria) * p(bank) p(Nigeria) * p(bank) + q(Nigeria) * q(bank) r(Nigeria,bank) = (0.2)(0.1) (0.2)(0.1) + (0.6)(0.025) = 0.930 This message will be rejected however since we set the threshold probability at 0.9. Concludes Bayes Reasoning 31

32. Probability Paradox I

33. Magic Dice: Or How to Win Every Time! • a) You select any one of the four dice (A, B, C, or D). • b) I’ll select another. • Both dice are thrown, highest number wins throw. • Do series of 10 throws. The person with the most • highest throws wins the series.(I.e. die “more likely to get a higher number” wins.) • Claim: In a game of 'The Best of Ten Throws’, I will almost certainly win • --- no matter which die you pick!! • Why is this strange? • Say, you pick die A. Let’s assume, die B is better. So, I pick B. • But, then, next game & next person picks B. Let’s assume C is better. I’ll select C. • Next person, will pick C. I’ll pick D. • Next person, will pick D… Hmm… • I’ll pick A and will win!! • A < B < C < D …. < A !! Failure of transitivity! But, could such a set of dice exist? Surprisingly, yes!

34. Magic Dice Prob(D wins over C) = 2/3 2/6 + (4/6)* (1/2) = 4/6 D Prob(A wins over D) = 4/6 = 2/3 A < B < C < D …. < A !! A C Prob(C wins over B) = 2/3 since 3/6 + (3/6)* (2/6) = 4/6 B Prob(B wins over A) = 4/6 = 2/3 (i.e. Prob(A wins over B) = 1/3) However: transitivity in expected value of dice throw E[B] < E[A] = E[C] < E[D] 16/6 < 18/6 = 18/6 < 20/6 Non-transitive dice: http://www.sciencenews.org/20020420/mathtrek.asp

35. Random Variables and Distributions

36. -2 0 2 S Random Variables For a given sample space S, a random variable(r.v.) is any real valued function on S, i.e., a random variable is a function that assigns a real number to each possible outcome Numbers Sample space Suppose our experiment is a roll of 2 dice. S is set of pairs. Example random variables: X = sum of two dice. X((2,3)) = 5 Y = difference between two dice. Y((2,3)) = 1 Z = max of two dice.Z((2,3)) = 3

37. Random variable • Suppose a coin is flipped three times. Let X(t) be the random variable that equals the number of heads that appear when t is the outcome. • X(HHH) = 3 • X(HHT) = X(HTH)=X(THH)=2 • X(TTH)=X(THT)=X(HTT)=1 • X(TTT)=0 Note: we generally drop the argument! We’ll just say the “random variable X”. And write e.g. P(X = 2) for “the probability that the random variable X(t) takes on the value 2”. Or P(X=x) for “the probability that the random variable X(t) takes on the value x.”

38. Distribution of Random Variable • Definition: • The distribution of a random variable X on a sample space S is the set of • pairs (r, p(X=r)) for all r  X(S), where p(X=r) is the probability that X • takes the value r. • A distribution is usually described specifying p(X=r) for each r  X(S). A probability distribution on a r.v. X is just an allocation of the total probability mass, 1, over the possible values of X.

39. The Birthday Paradox

40. 23 183 365 730 Birthdays A: 23 How many people have to be in a room to assure that the probability that at least two of them have the same birthday is greater than 1/2? Let pn be the probability that no people share a birthday among n people in a room. For L options answer is in the order of sqrt(L) ? Then 1 - pn is the probability that 2 or more share a birthday. We want the smallest n so that 1 - pn > 1/2. Informally, why?? Hmm. Why does such an n exist? Upper-bound?

41. Birthdays Assumption: Birthdays of the people are independent. Each birthday is equally likely and that there are 366 days/year Let pn be the probability that no-one shares a birthday among n people in a room. What is pn? (“brute force” is fine) Assume that people come in certain order; the probability that the second person has a birthday. Different than the first is 365/366; the probability that the third person has a different birthday. Form the two previous ones is 364/366.. For the jth person we have (366-(j-1))/366.

42. So, After several tries, when n=22 1= pn = 0.475. n=23 1-pn = 0.506 Relevant to “hashing”. Why?

43. Given a certain m, find the smallest n Such that the probability of a collision is greater than a particular threshold p. It can be shown that for p>1/2, n 1.177 From Birthday Problem to Hashing Functions • Probability of a Collision in Hashing Functions • A hashing function h(k) is a mapping of the keys (or records, e.g., SSN, around • 300x 106 in the US) to a much smaller storage location. A good hashing fucntio • yields few collisions. What is the probability that no two keys are mapped • to the same location by a hashing function? • Assume m is the number available storage locations, so the probability • of mapping a key to a location is 1/m. • Assuming the keys are k1, k2, kn, the probability of mapping the jth record to a • free location is after the first (j-1) records is (m-(j-1))/m. m = 10,000, gives n = 117. Not that many!