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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, and physics.Template:Cn

Definition

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

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

ρn(x1,,xn)=det(K(xi,xj)1i,jn)

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

Properties

Existence

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

ρk(xσ(1),,xσ(k))=ρk(x1,,xk)σSk,k
  • Positivity: For any N, and any collection of measurable, bounded functions φk:Λk → ℝ, k = 1,. . . ,N with compact support:
If
φ0+k=1Ni1ikφk(xi1xik)0 for all k,(xi)i=1k
Then
φ0+i=1NΛkφk(x1,,xk)ρk(x1,,xk)dx1dxk0 for all k,(xi)i=1k [2]

Uniqueness

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

k=0(1k!Akρk(x1,,xk)dx1dxk)1k=

for every bounded Borel A ⊆ Λ.[2]

Examples

Gaussian unitary ensemble

Mining Engineer (Excluding Oil ) Truman from Alma, loves to spend time knotting, largest property developers in singapore developers in singapore and stamp collecting. Recently had a family visit to Urnes Stave Church. The eigenvalues of a random m × m Hermitian matrix drawn from the Gaussian unitary ensemble (GUE) form a determinantal point process on with kernel

Km(x,y)=k=0m1ψk(x)ψk(y)

where ψk(x) is the kth oscillator wave function defined by

ψk(x)=12nn!Hk(x)ex2/4

and Hk(x) is the kth Hermite polynomial. [3]

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 ℤTemplate:Clarify + Template:Frac with the discrete Bessel kernel, given by:

K(x,y)={θk+(|x|,|y|)|x||y|if xy>0,θk(|x|,|y|)xyif xy<0,

where

k+(x,y)=Jx12(2θ)Jy+12(2θ)Jx+12(2θ)Jy12(2θ),
k(x,y)=Jx12(2θ)Jy12(2θ)+Jx+12(2θ)Jy+12(2θ)

For J the Bessel function of the first kind, and θ the mean used in poissonization.[4]

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).[2]

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.[5] Then the uniformly random spanning tree of G is a determinantal point process on E, with kernel

K(e,f)=Ie,If,e,fE.[1]

References

43 year old Petroleum Engineer Harry from Deep River, usually spends time with hobbies and interests like renting movies, property developers in singapore new condominium and vehicle racing. Constantly enjoys going to destinations like Camino Real de Tierra Adentro.

  1. 1.0 1.1 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.
  2. 2.0 2.1 2.2 A. Soshnikov, Determinantal random point fields. Russian Math. Surveys, 2000, 55 (5), 923–975.
  3. B. Valko. Random matrices, lectures 14–15. Course lecture notes, University of Wisconsin-Madison.
  4. A. Borodin, A. Okounkov, and G. Olshanski, On asymptotics of Plancherel measures for symmetric groups, available via http://xxx.lanl.gov/abs/math/9905032.
  5. Lyons, R. with Peres, Y., Probability on Trees and Networks. Cambridge University Press, In preparation. Current version available at http://mypage.iu.edu/~rdlyons/