Discrete probability distribution/Related Articles: Difference between revisions
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Revision as of 17:21, 11 September 2009
- See also changes related to Discrete probability distribution, or pages that link to Discrete probability distribution or to this page or whose text contains "Discrete probability distribution".
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- Conditioning (probability) [r]: Conditional probabilities, conditional expectations and conditional distributions are treated on three levels. [e]
- Continuous probability distribution [r]: Probability distribution where variables can take on arbitrary values in a continuum. [e]
- Entropy of a probability distribution [r]: A number that describes the degree of uncertainty or disorder the distribution represents. [e]
- Measure theory [r]: Generalization of the concepts of length, area, and volume, to arbitrary sets of points not composed of line segments or rectangles. [e]
- Poisson distribution [r]: a probability distribution that is typically used to model the number of independent events (occurring at a constant average rate) that fall within a stated interval. [e]
- Probability distribution [r]: Function of a discrete random variable yielding the probability that the variable will have a given value. [e]
- Sigma algebra [r]: A formal mathematical structure intended among other things to provide a rigid basis for measure theory and axiomatic probability theory. [e]
- Zipf distribution [r]: Observation that states that, in a population consisting of many different types, the proportion belonging to the nth most common type is approximately proportional to 1/n. [e]