• Discrete probability distribution counts events that have countable or final outcomes.

  • This is in contrast to a continuous distribution, where the results can fall anywhere on the continuum.
  • Common examples of a discrete distribution include the binomial distribution, the Poisson distribution, and the Bernoulli distribution.
  • These distributions often include a statistical analysis of the “numbers” or “how many times” an event occurs.
  • In finance, discrete distributions are used in valuing options and predicting market shocks or recessions.