<Li> The degenerate distribution at x, where X is certain to take the value x . This does not look random, but it satisfies the definition of random variable . This is useful because it puts deterministic variables and random variables in the same formalism . </Li> <Li> The discrete uniform distribution, where all elements of a finite set are equally likely . This is the theoretical distribution model for a balanced coin, an unbiased die, a casino roulette, or the first card of a well - shuffled deck . </Li> <Li> The hypergeometric distribution, which describes the number of successes in the first m of a series of n consecutive Yes / No experiments, if the total number of successes is known . This distribution arises when there is no replacement . </Li> <Li> The Poisson binomial distribution, which describes the number of successes in a series of independent Yes / No experiments with different success probabilities . </Li>

What are the different types of discrete probability distributions