Determinantal point process

In mathematics, a determinantal point process is a stochastic point process, the probability distribution of which is characterized as a determinant of some function. Such processes arise as important tools in random matrix theory, combinatorics, physics, and wireless network modeling.[1]

Definition

Let be a locally compact Polish space and be a Radon measure on . Also, consider a measurable function K2  ℂ.

We say that is a determinantal point process on with kernel if it is a simple point process on with a joint intensity or correlation function (which is the density of its factorial moment measure) given by

for every n ≥ 1 and x1, . . . , xn ∈ Λ.[2]

Properties

Existence

The following two conditions are necessary and sufficient for the existence of a determinantal random point process with intensities ρk.

If
Then
[3]

Uniqueness

A sufficient condition for the uniqueness of a determinantal random process with joint intensities ρk is

for every bounded Borel A  Λ.[3]

Examples

Gaussian unitary ensemble

The eigenvalues of a random m × m Hermitian matrix drawn from the Gaussian unitary ensemble (GUE) form a determinantal point process on with kernel

where is the th oscillator wave function defined by

and is the th Hermite polynomial. [4]

Poissonized Plancherel measure

The poissonized Plancherel measure on partitions of integers (and therefore on Young diagrams) plays an important role in the study of the longest increasing subsequence of a random permutation. The point process corresponding to a random Young diagram, expressed in modified Frobenius coordinates, is a determinantal point process on ℤ + 12 with the discrete Bessel kernel, given by:

where

For J the Bessel function of the first kind, and θ the mean used in poissonization.[5]

This serves as an example of a well-defined determinantal point process with non-Hermitian kernel (although its restriction to the positive and negative semi-axis is Hermitian).[3]

Uniform spanning trees

Let G be a finite, undirected, connected graph, with edge set E. Define Ie:E  2(E) as follows: first choose some arbitrary set of orientations for the edges E, and for each resulting, oriented edge e, define Ie to be the projection of a unit flow along e onto the subspace of 2(E) spanned by star flows.[6] Then the uniformly random spanning tree of G is a determinantal point process on E, with kernel

.[2]

References

  1. N. Deng, W. Zhou, and M. Haenggi. The Ginibre point process as a model for wireless networks with repulsion. IEEE Transactions on Wireless Communications, vol. 14, pp. 107-121, Jan. 2015.
  2. 1 2 Hough, J. B., Krishnapur, M., Peres, Y., and Virág, B., Zeros of Gaussian analytic functions and determinantal point processes. University Lecture Series, 51. American Mathematical Society, Providence, RI, 2009.
  3. 1 2 3 A. Soshnikov, Determinantal random point fields. Russian Math. Surveys, 2000, 55 (5), 923–975.
  4. B. Valko. Random matrices, lectures 14–15. Course lecture notes, University of Wisconsin-Madison.
  5. A. Borodin, A. Okounkov, and G. Olshanski, On asymptotics of Plancherel measures for symmetric groups, available via http://xxx.lanl.gov/abs/math/9905032.
  6. Lyons, R. with Peres, Y., Probability on Trees and Networks. Cambridge University Press, In preparation. Current version available at http://mypage.iu.edu/~rdlyons/
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