Probability distribution: Difference between revisions
imported>Ragnar Schroder (Adding the non-frequentist interpretation of distributions to the intro.) |
imported>Ragnar Schroder m (Changing layout.) |
||
| Line 8: | Line 8: | ||
== Important discrete probability distributions == | |||
[[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%. | [[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%. | ||
| Line 25: | Line 25: | ||
== Important continuous probability distributions == | |||
[[Gaussian distribution]] - Also known as the normal distribution. | [[Gaussian distribution]] - Also known as the normal distribution. | ||
Revision as of 00:56, 25 June 2007
Probability distributions is the main mathematical method of quantifying uncertainty. A probability distribution also represents the expected results of an experiment repeated multiple times.
As a simple example, 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).
There are two main classes of probability distributions: Discrete and continuous. Discrete distributions describe variables that take on discrete values only (typically the positive integers), while continuous distributions describe variables that can take on arbitrary values in a continuum (typically the real numbers).
In more advanced studies, one also comes across hybrid distributions.
Important discrete probability distributions
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.
Negative Binomial distribution -
Important continuous probability distributions
Gaussian distribution - Also known as the normal distribution.
Uniform continuous distribution -
Exponential distribution - Given a sequence of events, and the waiting time between two consequitive events is independent of how long we've already waited, the time between events follows the exponential distribution.