Discrete Probability Distribution
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.
f(x) are all probability mass function(pmf)
Discrete Uniform 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
Negative Binomial Distribution
x = occurrence of event given a time period
Sampling without replacement, exact n number of successes.
With replacement is binomial distribution.