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Conditional c.d.f. (discrete random variable) If X and Y are discrete random variables, the conditional (cumulative) probability distribution function of X given Y=y is FX|Y(x|y)=P(Xx|Y=y)=xxpX|Y(x|y).
Discrete events are those with a finite number of outcomes, e.g. tossing dice or coins. For example, when we flip a coin, there are only two possible outcomes: heads or tails. When we roll a six-sided die, we can only obtain one of six possible outcomes, 1, 2, 3, 4, 5, or 6.
The probabilities in the probability distribution of a random variable X must satisfy the following two conditions: Each probability P(x) must be between 0 and 1: 0P(x)1. The sum of all the possible probabilities is 1: P(x)=1.
Anything that can be counted (in whole numbers) has a discrete probability distribution. Like the number of heads if you flip a coin 100 times. The outcome of rolling dice. The number of balls drawn from a bag before a red ball is drawn.
A discrete probability distribution counts occurrences that have countable or finite outcomes. Discrete distributions contrast with continuous distributions, where outcomes can fall anywhere on a continuum. Common examples of discrete distribution include the binomial, Poisson, and Bernoulli distributions.
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An example of a discrete probability is the probability of rolling a 3 on a fair six-sided die. Since the possible outcomes are limited to 1, 2, 3, 4, 5, and 6, and each outcome has an equal probability of occurring, the probability of rolling a 3 is 1/6.
Examples of discrete random variables include the number of children in a family, the Friday night attendance at a cinema, the number of patients in a doctors surgery, the number of defective light bulbs in a box of ten.
Conditional probability: p(A|B) is the probability of event A occurring, given that event B occurs. For example, given that you drew a red card, whats the probability that its a four (p(four|red))=2/26=1/13. So out of the 26 red cards (given a red card), there are two fours so 2/26=1/13.

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