# Discrete Probability Distribution

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Probability mass function -> Discrete (finite number of different values)

Probability density function -> Continuous (every value in an interval)

Both have cumulative distribution function, where f(x) = P(X<x)

The inverse of the CDF is called quantile function, and it is useful for indicating where the probability is located in a distribution.

# Discrete Distributions

f(x) are all probability mass function(pmf)

## Discrete Uniform Distribution

## Bernoulli Distribution

## A sequence of Bernoulli trials -> Binomial Distribution

If N is large and P is small, we can expect B(n,p) = Poisson(λ) and λ = n * p

Poisson in this case can be an alternative

## Geometric Distribution

## Negative Binomial Distribution

## Poisson Distribution

x = occurrence of event given a time period

## Hypergeometric Distribution

Sampling without replacement, exact n number of successes.

With replacement is binomial distribution.