.. title: Engineering Probability Class 6 Thurs 2019-01-31 .. slug: class06 .. date: 2019-01-31 .. tags: mathjax .. category: class .. link: .. description: .. type: text .. sectnum:: .. contents:: Table of contents .. Bayes theorem ctd -------------------------- #. `Wikipedia on Bayes theorem `_. We'll do these examples in class. #. Example 2.28, page 51. #. Example 2.30, page 53, chip quality control: For example 2.28, how long do we have to burn in chips so that the survivors have a 99% probability of being good? p=0.1, a=1/20000. Iclicker review of Bayes theorem -------------------------------- #. Event A is that a random person has a lycanthopy gene. Assume P(A) = .01. Genes-R-Us has a DNA test for this. B is the event of a positive test. There are false positives and false negatives each w.p. (with probability) 0.1. That is, P(B|A') = P(B' | A) = 0.1 What's P(A')? a. 0.09 #. .099 #. .189 #. .48 #. .99 #. What's P(A and B)? a. 0.09 #. .099 #. .189 #. .48 #. .99 #. What's P(A' and B)? a. 0.09 #. .099 #. .189 #. .48 #. .99 #. What's P(B)? a. 0.09 #. .099 #. .189 #. .48 #. .99 #. You test positive. What's the probability you're really positive, P(A|B)? a. 0.09 #. .099 #. .189 #. .48 #. .99 Chapter 2 ctd: Independent eventa --------------------------------- #. 2.5 Independent events a. :math:`P[A\cap B] = P[A] P[B]` #. P[A|B] = P[A], P[B|A] = P[B] #. A,B independent means that knowing A doesn't help you with B. #. Mutually exclusive events w.p.>0 must be dependent. #. Example 2.33, page 56. .. image:: /images/fig214.jpg #. More that 2 events: a. N events are independent iff the occurrence of no combo of the events affects another event. #. Each pair is independent. #. Also need :math:`P[A\cap B\cap C] = P[A] P[B] P[C]` #. *This is not intuitive* A, B, and C might be pairwise independent, but, as a group of 3, are dependent. #. See example 2.32, page 55. A: x>1/2. B: y>1/2. C: x>y #. Common application: independence of experiments in a sequence. #. Example 2.34: coin tosses are assumed to be independent of each other. P[HHT] = P[1st coin is H] P[2nd is H] P[3rd is T]. #. Example 2.35, page 58. System reliability a. Controller and 3 peripherals. #. System is up iff controller and at least 2 peripherals are up. #. Add a 2nd controller. #. 2.6 p59 Sequential experiments: *maybe* independent #. 2.6.1 Sequences of independent experiments a. Example 2.36 #. 2.6.2 Binomial probability a. *Bernoulli trial* flip a possibly unfair coin once. *p* is probability of head. #. (Bernoulli did stats, econ, physics, ... in 18th century.) #. Example 2.37 a. P[TTH] = :math:`(1-p)^2 p` #. P[1 head] = :math:`3 (1-p)^2 p` #. Probability of exactly k successes = :math:`p_n(k) = {n \choose k} p^k (1-p)^{n-k}` #. :math:`\sum_{k=0}^n p_n(k) = 1` #. Example 2.38 #. Can avoid computing n! by computing :math:`p_n(k)` recursively, or by using approximation. Also, in C++, using double instead of float helps. (Almost always you should use double instead of float. It's the same speed.) #. Example 2.39 #. Example 2.40 Error correction coding #. Multinomial probability law a. There are M different possible outcomes from an experiment, e.g., faces of a die showing. #. Probability of particular outcome: :math:`p_i` #. Now run the experiment n times. #. Probability that i-th outcome occurred :math:`k_i` times, :math:`\sum_{i=1}^M k_i = n` .. math:: P[(k_1,k_2,...,k_M)]` :math:`= \frac{n!}{k_1! k_2! ... k_M!} p_1^{k_1} p_2^{k_2}...p_M^{k_M} #. Example 2.41 dartboard. #. Example 2.42 random phone numbers. #. 2.7 Computer generation of random numbers a. Skip this section, except for following points. #. Executive summary: it's surprisingly hard to generate good random numbers. Commercial SW has been known to get this wrong. By now, they've gotten it right (I hope), so just call a subroutine. #. Arizona lottery got it `wrong `_ in 1998. #. Even random electronic noise is hard to use properly. The best selling 1955 book `A Million Random Digits with 100,000 Normal Deviates `_ had trouble generating random numbers this way. Asymmetries crept into their circuits perhaps because of component drift. For a laugh, read the reviews. #. Pseudo-random number generator: The subroutine returns numbers according to some algorithm (e.g., it doesn't use cosmic rays), but for your purposes, they're random. #. Computer random number routines usually return the same sequence of number each time you run your program, so you can reproduce your results. #. You can override this by *seeding* the generator with a genuine random number from linux /dev/random. #. 2.8 and 2.9 p70 Fine points: Skip. #. Review Bayes theorem, since it is important. Here is a fictitious (because none of these probilities have any justification) SETI example. a. A priori probability of extraterrestrial life = P[L] = :math:`10^{-8}`. #. For ease of typing, let L' be the complement of L. #. Run a SETI experiment. R (for Radio) is the event that it has a positive result. #. P[R|L] = :math:`10^{-5}`, P[R|L'] = :math:`10^{-10}`. #. What is P[L|R] ? #. Some specific probability laws a. In all of these, successive events are independent of each other. #. A **Bernoulli** trial is one toss of a coin where *p* is probability of head. #. We saw binomial and multinomial probilities in class 4. #. The **binomial** law gives the probability of exactly *k* heads in *n* tosses of an unfair coin. #. The **multinomial** law gives the probability of exactly **ki** occurrances of the i-th face in n tosses of a die. #. iClicker: You have a coin where the probability of a head is p=2/3 If you toss it twice, what's the probability that you will see one head and one tail? a. 1/2 #. 1/3 #. 2/9 #. 5/9 #. 4/9