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[[File:AtomisationCl3DvdW.pngthumb300pxIn some cases, [[statistical physics]] uses ''probability measures'', but not all [[measure theorymeasures]] it uses are probability measures.<ref name=stern>''A course in mathematics for students of physics, Volume 2'' by Paul Bamberg, Shlomo Sternberg 1991 ISBN 0521406501 [http://books.google.com/books?id=eSmC4qQ0SCAC&pg=PA802 page 802]</ref><ref name= gut>''The concept of probability in statistical physics'' by Yair M. Guttmann 1999 ISBN 0521621283 [http://books.google.com/books?id=Q1AUhivGmyUC&pg=PA149 page 149]</ref>]]
A '''probability measure''' is a [[realvalued function]] defined on a set of events in a [[probability space]] that satisfies [[Measure (mathematics)measure properties]] such as ''countable additivity''.<ref>''An introduction to measuretheoretic probability'' by George G. Roussas 2004 ISBN 0125990227 [http://books.google.com/books?id=J8ZRgCNSwcC&pg=PA47 page 47]</ref>
Intuitively, the probability assigned to the union of two disjoint events by the measure should be the sum of the probabilities of the events, e.g. the value assigned to "Heads or Tails" in a coin toss should be the sum of the values assigned to Heads and Tails.
:<math>P(B \mid A) = \frac{P(A \cap B)}{P(A)}.</math>
satisfies the probability measure requirements.<ref>''Probability, Random Processes, and Ergodic Properties'' by Robert M. Gray 2009 ISBN 1441910891 [http://books.google.com/books?id=xVbL8mZWl8C&pg=PA163 page 163]</ref>
Probability measures are distinct from the more general notion of [[Fuzzy measure theoryfuzzy measures]] in which there is no requirement that the fuzzy values sum up to 1, and the additive property is replaced by an order relation based on [[set inclusion]].
==Example applications==
''Market measures'' which assign probabilities to [[financial market]] spaces based on actual market movements are examples of probability measures which are of interest in [[mathematical finance]], e.g. in the pricing of [[financial derivative]]s.<ref>''Quantitative methods in derivatives pricing'' by Domingo Tavella 2002 ISBN 0471394475 [http://books.google.com/books?id=dHIMulKy8dYC&pg=PA11 page 11]</ref> For instance, a [[riskneutral measure]] is a probability measure which assumes that the current value of assets is the [[expected value]] of the future payoff [[discounted]] at the [[riskfree rate]]. If there is a unique probability measure that must be used to price assets in a market, then the market is called a [[complete market]].<ref>''Irreversible decisions under uncertainty'' by Svetlana I. Boyarchenko, Serge Levendorskiĭ 2007
Not all measures that intuitively represent chance or likelihood are probability measures. For instance, although the fundamental concept of a system in [[statistical mechanics]] is a measure space, such measures are not always probability measures.<ref
Probability measures are also used in [[mathematical biology]].<ref>''Mathematical Methods in Biology'' by J. David Logan, William R. Wolesensky 2009 ISBN 0470525878 [http://books.google.com/books?id=6GGyquH8kLcC&pg=PA195 page 195]</ref> For instance, in comparative [[sequence analysis]] a probability measure may be defined for the likelihood that a variant may be permissible for an [[amino acid]] in a sequence.<ref>''Discovering biomolecular mechanisms with computational biology'' by Frank Eisenhaber 2006 ISBN 0387345272 [http://books.google.com/books?id=Pygg7cIZTwIC&pg=PA127 page 127]</ref>
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==References==
==Further reading==
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[[Category:Measures (measure theory)]]
