Time-lapse photography

2014-02-23T22:41:14Z

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Compound probability distribution

2014-01-25T15:29:31Z

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{{Expert-subject|Statistics|date=December 2010}}

In [[statistics]], a '''Pólya urn model''' (also known as a '''Pólya urn scheme''' or simply as '''Pólya's urn'''), named after [[George Pólya]], is a type of [[statistical model]] used as an idealized [[thought experiment|mental exercise]] framework, unifying many treatments.

In an [[urn model]], objects of real interest (such as atoms, people, cars, etc.) are represented as colored balls in an [[urn]] or other container. In the basic Pólya urn model, the urn contains ''x'' white and ''y'' black balls; one ball is drawn randomly from the urn and its color observed; it is then replaced in the urn, and an additional ball of the same color is added to the urn, and the selection process is repeated. Questions of interest are the evolution of the urn population and the sequence of colors of the balls drawn out.

This endows the urn with a self-reinforcing property sometimes expressed as ''[[the rich get richer (statistics)|the rich get richer]]''.

Note that in some sense, the Pólya urn model is the "opposite" of the model of [[sampling without replacement]]. When sampling without replacement, every time a particular value is observed, it is less likely to be observed again, whereas in a Polya urn model, an observed value is ''more'' likely to be observed again. In both of these models, the act of measurement has an effect on the outcome of future measurements. (For comparison, when [[sampling with replacement]], observation of a particular value has no effect on how likely it is to observe that value again.) Note also that in a Polya urn model, successive acts of measurement over time have less and less effect on future measurements, whereas in sampling without replacement, the opposite is true: After a certain number of measurements of a particular value, that value will never be seen again.

One of the reasons for interest in ''this particular'' rather elaborate urn model (i.e. with duplication and then replacement of each ball drawn) is that it provides an example in which the count (initially ''x'' black and ''y'' white) of balls in the urn is ''not'' concealed, which is able to approximate the correct updating of ''subjective'' probabilities appropriate to a ''different'' case in which the original urn content ''is'' concealed while ordinary sampling with replacement is conducted (without the Polya ball-duplication). Because of the simple "sampling with replacement" scheme in this second case, the urn content is now ''static'', but this greater simplicity is compensated for by the assumption that the urn content is now ''unknown'' to an observer. A Bayesian analysis of the observer's uncertainty about the urn's initial content can be made, using a ''particular choice'' of (conjugate) prior distribution. Specifically, suppose that an observer knows that the urn contains only identical balls, each coloured either black or white, but he does not know the absolute number of balls present, nor the proportion that are of each colour. Suppose that he holds prior beliefs about these unknowns: for him the probability distribution of the urn content is well approximated by some prior distribution for the total number of balls in the urn, and a beta prior distribution with parameters ''(x,y)'' for the initial proportion of these which are black, this proportion being (for him) considered approximately independent of the total number. Then the process of outcomes of a succession of draws from the urn (with replacement but without the duplication) has ''approximately the same probability law'' as does the above Polya scheme in which the actual urn content was not hidden from him. The approximation error here relates to the fact that an urn containing a known finite number ''m'' of balls of course cannot have an ''exactly'' beta-distributed unknown proportion of black balls, since the domain of possible values for that proportion are confined to being multiples of <math>1/m</math>, rather than having the full freedom to assume any value in the continuous unit interval, as would an ''exactly'' beta distributed proportion. This slightly informal account is provided for reason of motivation, and can be made more mathematically precise.

This basic '''Pólya urn model''' model has been enriched and generalized in many ways.

==Distributions related to the Polya urn==

* [[beta-binomial distribution]]: The distribution of the number of successful draws (trials), e.g. number of extractions of white ball, given <math>n</math> draws from a Polya urn.
* [[Dirichlet-multinomial distribution]] (also known as the ''multivariate Pólya distribution''): The distribution over the number of balls of each color, given <math>n</math> draws from a Polya urn where there are <math>k</math> different colors instead of only two.
* [[martingale (probability theory)|martingales]], the [[Beta-binomial distribution]] and the [[beta distribution]]: Let ''w'' and ''b'' be the number of white and black balls initially in the urn, and <math>w+n_w</math> the number of white balls currently in the urn after ''n'' draws. Then the sequence of values <math>\frac{w+n_w}{w+b+n}</math> for <math>n=1,2,3,\dots</math> is a normalized version of the [[Beta-binomial distribution]]. It is a [[martingale (probability theory)|martingale]] and converges to the [[beta distribution]] when ''n'' → ∞.
* [[Dirichlet process]], [[Chinese restaurant process]], '''Hoppe urn''': Imagine a modified Polya urn scheme as follows. We start with an urn with <math>\alpha</math> black balls. When drawing a ball from the urn, if we draw a black ball, put the ball back along with a new ball of a new non-black color randomly generated from a [[uniform distribution (discrete)|uniform distribution]] over an infinite set of available colours, and consider the newly generated color to be the "value" of the draw. Otherwise, put the ball back along with another ball of the same color, as for the standard Polya urn scheme. The colors of an infinite sequence of draws from this modified Polya urn scheme follow a [[Chinese restaurant process]]. If, instead of generating a new color, we draw a random value from a given base distribution and use that value to label the ball, the labels of an infinite sequence of draws follow a [[Dirichlet process]].<ref>{{ cite journal | author=Hoppe, F. | title=Polya-like urns and the Ewing sampling formula | year=1984 | journal=Journal of Mathematical Biology | volume=20 | page=91 }}</ref>
* [[Moran_model]]: An urn model used to model [[genetic_drift]] in theoretical [[population genetics]]. This is closely similar to the Polya urn model except that, in addition to adding a new ball of the same color, a randomly drawn ball is removed from the urn. The number of balls in the urn thus remains constant. Continued sampling then leads ultimately to an urn with all balls of one color, the probability of each color being the proportion of that color in the original urn. There are variants of the Moran model that insist that the ball removed from the urn be a different ball from one originally sampled in that step, and variants that do the removal of a ball immediately after the new ball is placed in the urn, so that the new ball is one of the balls available to be removed. This makes a small difference in the time taken to reach the state in which all balls are the same color. The Moran process models genetic drift in a population with overlapping generations.

==See also==
* [[Pitman–Yor process]]
* [[Moran_process]]

==References==
{{reflist}}

2. F. Alajaji and T. Fuja, "A Communication Channel Modeled on Contagion," IEEE Transactions on Information Theory, Vol. 40, pp. 2035-2041, November 1994.

3. A. Banerjee, P. Burlina and F. Alajaji, "Image Segmentation and Labeling Using the Polya Urn Model," IEEE Transactions on Image Processing, Vol. 8, No. 9, pp. 1243-1253, September 1999.

==Bibliography==
* N.L. Johnson and S.Kotz, (1977) "Urn Models and Their Application." John Wiley.
* Hosam Mahmoud, (2008) "Pólya Urn Models." Chapman and Hall/CRC. ISBN 978-1420059830.

[[Category:Probability theory]]

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Time-lapse photography

Compound probability distribution