1. Discrete Random Variables

2. Introduction In previous lectures we established a foundation of the probability theory; we applied the probability theory to some applications; we covered combinatorial problems; we studied Bayesian theorem. In this lecture we will define and describe the random variables; we will give a definition of probability mass function and probability distribution functions; a number of discrete probability mass functions will be given. Properties of probability mass and cumulative functions will be given.

3. Definition of Discrete Random Variables Sample space of a die is {1,2,3,4,5,6}, because each face of a die has a dot pattern containing 1,2,3,4,5, of 6 dots. This type of experiment is called numerical valued random phenomenon. What if outcomes of an experiment are nonnumerical? The sample space of a coin toss is S = {tail, head}. It is often useful to assign numeric values to such experiments. Example: the total number of heads observed can be found as

4. Definition of Discrete Random Variables The function that maps S into S X and which is denoted by X(.) is called a random variable. The name random variable is a poor one in that the function X(.) is not random but a known one and usually one of our own choosing. Example: In phase-shift keyed (PSK) digital system a ”0” or “1” is communicated to a receiver by sending A 1 or a 0 occurs with equal probability so we model the choice of a bit as a fair coin tossing experiment. random variable

5. Definition of Discrete Random Variables Random variable is a function that maps the sample space S into a subset of the real line. S  S X For a discrete random variable this subset is a finite or countable infinite set of points. A discrete random variable may also map multiple elements of the sample space into the same number. One-to-one Many-to-one Example:

6. Probability of Discrete Random Variables What is the probability P[X(s i ) = x i ] for each ? If X(.) maps each s i into a different x i (or X(.) one-to-one), then because s i and x i are just two different names for the same event If, there multiple outcomes in S that map into the same value x i (X(.) is many-to-one) then s j ’s are simple events in S and are therefore mutually exclusive. The probability mass function (PMF) can be summarized as Random variable (RV) [] discrete RV The PMF is the probability that the RV X takes on the value x i for each possible x i.

7. Examples Coin toss – one-to-one mapping The experiment consists of a single coin toss with a probability of heads equal to p. The sample space is S = {head, tail} and the RV is The PMF is therefore Die toss – many-to-one mapping The experiment consists of a single fair die toss. With a sample space of S = (1,2,3,4,5,6} and in interest only in whether the outcome is even or odd we define the RV as The event is just the subset of S for which X(s) = x i holds.

8. Properties Because PMFs p X [x i ] are just new names for the probability P[X(s) =x i ] they must satisfy the usual properties: Property 1: Range values Property 2: Sum of values If S X consists of M outcomes If S X is countably infinite. We will omit the s argument of X to write p X [x i ] = P[X = x i ]. Thus, we probability of event A defined on S X is given by

9. Example Calculating probabilities based on the PMF Consider a die with sides S = {side1, side2, side3, side4, side5, side6} whose sides have been labeled as with 1,2 or 3 dots. Thus, Assume we are interested in the probability that a 2 or 3 occurs or A = {2,3}, then

10. Important Probability Mass Functions Bernoulli ~ is distributed according to Binomial p =0.25 M = 10 p =0.25 (M + 1)p max

11. Important Probability Mass Functions Geometric Poisson p =0.25

12. Approximation of Binomial PMF by Poisson PMF If in a binomial PMF, M  ∞ as p  0 such that the product λ = Mp remains constant, then bin(M,p)  Pois(λ) and λ represents average number of successes in M Bernoulli trials. In other words, by keeping the average number of successes fixed but assuming more and more trials with smaller and smaller probabilities of success on each trial, leads to a Poisson PMF. M = 10 p =0.5 M = 100 p =0.05

13. Approximation of Binomial PMF by Poisson PMF We have for the binomial PMF with p = λ/M  0 as M  ∞. For a fixed k, as M ,we have that and. Using L’Hospital’s rule we have and therefore

14. Transformation of Discrete Random Variable It is frequently of interest to be able to determine the PMF of the new random variable Y = g(X). Consider a die which sides are labeled with the numbers 0,0,1,1,2,2. Then the transformation appears as many-to-one

15. Examples In general, Example: One-to-one transformation of Bernoulli random variable. If X ~ Ber(p) and Y = 2X – 1, determine the PMF of Y. The sample space S X = {0, 1} and consequently S Y = {-1, 1}. It follows that x 1 = 0 maps into y 1 = -1 and x 2 = 1 maps into y 2 = 1. Thus, Example: Many-to-one transformation Let the transformation be Y = g(X) = X 2 which is defined on the sample space S X = {-1, 0, 1} so that S Y = {0, 1}. Clearly, g(x j ) = x j 2 = 0 only for x j = 0. However, g(x j ) = x j 2 = 1 for x j = -1 and x j = 1. Thus, using (*) we have

16. Example: Many-to-one transformation of Poisson random variable Consider X ~ Pois(λ) and define the transformation Y = g(X) as To find the PMF for Y we use We need only determine since. Thus, Taylor expansion

17. Cumulative Distribution Function An alternative means of summarizing the probabilities of a discrete random variable is the cumulative distribution function (CDF). The CDF for a RV X is defined as Note that the value X = x is included in the interval. Example: if X ~ Ber(p), then the PMF and the corresponding CDF are PMFCDF p = 0.25

18. Example: CDF for geometric random variable Since for k = 1,2,…, we have the CDF Where [x] denotes the largest integer not exceeding x. The PMF can be recovered from CDF by.

19. Example: CDF for geometric random variable PMF and CDF are equivalent descriptions of the probability and can be used to find the probability of X being in an interval. To determine for geometric RV we have In general, the intervals (a,b] will have different from (a,b), [a, b), and [a,b] probabilities. but

20. Properties of CDF Property 3. CDF is between 0 and 1 Proof: Since by def. F X (x) = P[X ≤ x] is a probability for all x, it must lie between 0 and 1. Property 4. Limits of CDF as x  -∞ and x  ∞. Proof: since the values that X(s) can take on do not include -∞. Also since the values that X(s) can take on are all included on the real line

21. Properties of CDF Property 5. A monotonically increasing function g(.) is one in which for every x 1 and x 2 with x 1 ≤ x 2, it follows that g(x 1 ) ≤ g(x 2 ). Proof: Property 6. CDF is right-continuous By right-continuous is meant, that as we approach the point x0 from the right, the limiting value of the CDF should be the value of the CDF at that point (definition) (Axiom 3) (def. and Axiom 1)

22. Properties of CDF Property 7. Probability of interval found using the CDF Proof: Since a < b and the intervals on the right-hand-side are disjoint, by Axiom 3 Or rearranging the terms we have that

23. Computer Simulations Assume that X can take on values S X = {1,2,3} with PMF

24. Computer Simulations u is a random variable whose values are equally likely to fall within the interval (0,1). It is called uniform RV. To estimate the PMF a relative frequency interpretation of probability will yield The CDF is estimated for all x via k = 1,2,3 M = 100

25. Computer Simulations Note that an inverse CDF is given by so we choose the value of x as shown below.

26. Exercise 1.Draw a picture depicting a mapping of the outcome of a clock hand that is randomly rotated with discrete steps 1 to 12. 2.Consider a random experiment for which S = {s i : s i = -3, -2, -1, 0, 1, 2, 3} and the outcomes are equally likely. If a random variable is defined as X(s i ) = s i 2, find S x and the PMF.

27. Exercise 1.If X is a geometric RV with p =0.25, what is the probability that X ≥ 4? 2.Compare the PMFs for Pois(1) and bin(100, 0.01) RVs. 3.Generate realizations of a Pois(1) RV by using a binomial approximation. 4.Find and plot the CDF of Y = - X if X ~ Ber(1/4). pois

28. Homework problems 1.A horizontal bar of negligible weight is loaded with three weights as shown in Figure. Assuming that the weights are concentrated at their center locations, plot the total mass of the bar starting at the left (where x = 0 meters) to any point on the bar. How does this relate to a PMF and a CDF? 2.Find the PMF if X is a discrete random variable with the CDF

29. Homework problems 3.Prove that the function g(x) = exp(x) is a monotonically increasing function by showing that g(x 2 ) ≥ g(x 1 ) if x 2 ≥ x 1. 4.Estimate the PMF for a geom(0.25) RV for k=1,2,….,20 using a computer simulation and compare it to the true PMF. Also, estimate the CDF from your computer simulation.