Discrete Probability Distribution

• In probability theory, the term event is the set of outcomes from an experiment.
• Probability tells us how often some event will happen after many repeated trials. We are not totally certain about the outcome of any event. The best we can say is how likely they are to happen.
• A discrete probability distribution, also known as the probability mass function (PMF) is a listing of all possible values of a discrete random variable along with their corresponding probabilities in a given sample space.
• A PMF of a discrete random variable may be a table, a formula or a graph that shows all the possible values of the random variable, along with the corresponding probabilities of those values. Values of the random variables are denoted by X and probabilities are denoted by P(X).
• Properties of Probability Distribution

  1. The total probability of all values should be equal to 1.
  2. The probability of each value should be more than or equal to 0 and less than or equal to 1. That is, 0 ≤ P(X) ≤ 1.
• Probability mass function or PMF is a table, a formula, or a graph showing the probability distribution of a discrete random variable, denoted by P(X).