# Difference between revisions of "Science:MATH105 Probability/Lesson 1 DRV/1.01 Discrete Random Variables"

In many areas of science we are interested in quantifying the probability that a certain outcome of an experiment occurs. We can use a random variable to identify numerical events that are of interest in an experiment. In this way, a random variable is a theoretical representation of the physical or experimental process we wish to study. More precisely, a random variable is a quantity without a fixed value, but which can assume different values depending on how likely these values are to be observed; these likelihoods are probabilities.

To quantify the probability that a particular event occurs, we use a number between 0 and 1. A probability of 0 implies that the event cannot occur, whereas a probability of 1 implies that the event must occur. Any value in the interval (0, 1) means that the event will only occur some of the time. Equivalently, if an event occurs with probability p, then this means there is a p(100)% chance of observing this event.

Conventionally, we denote random variables by capital letters, and particular values that they can assume by lowercase letters. So we can say that X is a random variable that can assume certain particular values x with certain probabilities.

When there is a discrete list of events that can occur, we can use the notation Pr(X = xk) to denote the probability that the random variable X assumes the particular value xk. Note that a discrete list of events means that the random variable X can assume only finitely many or countably many values, meaning we should be able to list the values that X can take, even if this list is infinite (as with a list of all positive integers).

This is in contrast to a continuous random variable, where the values the random variable can assume are given by a continuum of values (for example, we could define a random variable that can take any value in the interval [1,2]). We will discuss continuous random variables in detail in the second part of this module. For now, we deal strictly with discrete random variables.

Discrete Probability Rules
1. Probabilities are numbers between 0 and 1: 0 ≤ pk ≤ 1 for all k
2. The sum of all probabilities for a given experiment is equal to one: ∑k pk = 1
3. The probability of an event is 1 minus the probability that any other event occurs: pj = 1 - ∑k≠ j pk

## Example: Tossing a Fair Coin Once

If we toss a coin into the air, there are only two possible outcomes: it will land as either "heads" (H) or "tails" (T). If the tossed coin is a "fair" coin, it is equally likely that the coin will land as tails or as heads. In other words, there is a 50% chance that the coin will land heads, and a 50% chance that the coin will land tails.

Using our notation for the probability of a discrete event, we can assign

• p0 to be the probability that the tossed coin will land as heads
• p1 to be the probability that the tossed coin will land as tails

Because there are two outcomes that are equally likely, we assign the probability of 0.5 to each of them.

• p0 = 1/2
• p1 = 1/2

As required, the sum of the probabilities equals 1, and each probability is a number in the interval [0, 1]. Notice that p0 = 1 - p1.

We can define the random variable X to represent this coin tossing experiment. That is, X is the discrete random variable that takes the value 0 with probability p0 and takes the value 1 with probability p1. Notice that with this notation, the experimental event that "we toss a fair coin and observe heads" is the same as the theoretical event that "the random variable X is observed to take the value 0". We say that X is a Bernoulli random variable with parameter p0 = 1/2 and can write X ~ Ber(1/2).

## Example: Tossing a Fair Coin Twice

Similarly, if we toss a fair coin two times, there are four possible outcomes. Each outcome is a sequence of heads (H) or tails (T):

• HH
• HT
• TH
• TT

Using our notation for probability, we can assign

• p0 to be the probability that the outcome will be HH
• p1 to be the probability that the outcome will be HT
• p2 to be the probability that the outcome will be TH
• p3 to be the probability that the outcome will be TT

Because the coin is fair, each outcome is equally likely to occur. There are 4 possible outcomes, so we assign each outcome a probability of 1/4. That is, p0 = p1 =p2 =p3 = 1/4.

Equivalently, we notice that for any of the four possible events to occur, we must observe two distinct events from two separate flips of a fair coin. So for example, to observe the sequence HH, we must flip a fair coin once and observe H, then flip a fair coin again and observe H once again. (We say that these two events are independent since the outcome of one event has no effect on the outcome of the other.) Since the probability of observing H after a flip of a fair coin is 1/2, we see that the probability of observing the sequence HH should be (1/2)×(1/2) = 1/4.

Observe that again, all of our probabilities sum to 1, and each probability is a number on the interval [0, 1]. If we define the random variable Y to represent this new coin tossing experiment, we see that Y takes the value 0 with probability p0 = 1/4, 1 with probability p1 = 1/4, 2 with probability p2 = 1/4, and 3 with probability p3 = 1/4. Notice that with this notation, the experimental event that "we toss two fair coins and observe first tails, then heads" is the same as the theoretical event that "the random variable Y is observed to take the value 2". We say that Y is a uniform discrete random variable with parameter 4 since Y takes each of its four possible values with equal, or uniform, probability. To denote this distributional relationship, we can write Y ~ Uniform(4).