Discrete probability distribution

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A discrete probability distribution is a one class of probability distributions. The other main class in basic probability theory is continuous probability distributions.


[edit] Intro - probability as logic viewpoint

Faced with a list of mutually exclusive propositions or possible outcomes, people intuitively put "degrees of belief" on the different alternatives.

For instance, consider the following 2 propositions:

Based on available information about the record of past weather in England, people tend intuitively to put more "belief" in the first possibility than the second.

For another, slightly more complex example, consider the following 6 propositions:

Based on local information about past traffic patterns, people will intuitively distribute a "degree of belief" among the propositions.

If every "degree of belief" is a real numbers ranging from 0 to 1 and their sum is exactly 1, we have a discrete probability distribution, and each "degree of belief" is called a probability.

A discrete probability distribution is thus nothing more than a mathematically precise version of a common intuitive phenomenon, reflecting the human mind's ability to deduce and infer the physical propensities of external systems.

As a simple illustration as to how the individual probabilities may be obtained in practice, consider the expected results for a coin toss experiment. While we don't know the results for any individual toss of the coin, we can expect the results to average out to be heads half the time and tails half the time (assuming a fair coin).

[edit] Formal definition

Given a countable set S={s0, ... ,sn, ... } of mutually exclusive propositions (or possible outcomes of an experiment). Let A=[0,1}, a proper subset of the real numbers R. A discrete probability distribution is then a subset T={(s0,t0),...,(sn,tn), ...} of the cartesian product S \times A, such that all the ti sum to exactly 1.

[edit] Important examples

Bernoulli distribution - Each experiment is either a 1 ("success") with probability p or a 0 ("failure") with probability 1-p. An example would be tossing a coin. If the coin is fair, your probability for "success" will be exactly 50%.

An experiment where the outcome follows the Bernoulli distribution is called a Bernoulli trial.

Binomial distribution - Each experiment consists of a series of identical Bernoulli trials, f.i. tossing a coin n times, and counting the number of successes.

Uniform distribution - Each experiment has a certain finite number of possible outcomes, each with the same probability. Throwing a fair die, f.i., has six possible outcomes, each with the same probability. The Bernoulli distribution with p=0.5 is another example.

Poisson distribution - Given an experiment where we have to wait for an event to happen, and the expected remainding waiting time is independent of how long we've already waited. Then the number of events per unit time will be a Poisson distributed variable.

Geometric distribution -

Negative Binomial distribution -

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