## simulating determinantal processes

In the plane to Atlanta, I happened to read a paper called Efficient simulation of the Ginibre point process by Laurent Decreusefond, Ian Flint, and Anaïs Vergne (from Telecom Paristech). “Happened to” as it was a conjunction of getting tipped by my new Dauphine colleague (and fellow blogger!) Djalil Chaffaï about the paper, having downloaded it prior to departure, and being stuck in a plane (after watching the only Chinese [somewhat] fantasy movie onboard, Saving General Yang).

This is mostly a mathematics paper. While indeed a large chunk of it is concerned with the rigorous definition of this point process in an abstract space, the last part is about simulating such processes. They are called determinantal (and not detrimental as I was tempted to interpret on my first read!) because the density of an n-set (x1x2,…,xn) is given by a kind of generalised Vandermonde determinant

$p(x_1,\ldots,x_n) = \dfrac{1}{n!} \text{det} \left( T(x_i,x_j) \right)$

where T is defined in terms of an orthonormal family,

$T(x,y) = \sum_{i=1}^n \psi_i(x) \overline{\psi_i(y)}.$

(The number n of points can be simulated via an a.s. finite Bernoulli process.) Because of this representation, the sequence of conditional densities for the xi‘s (i.e. x1, x2 given x1, etc.) can be found in closed form. In the special case of the Ginibre process, the ψi‘s are of the form

$\psi_i(z) =z^m \exp\{-|z|^2/2\}/\sqrt{\pi m!}$

and the process cannot be simulated for it has infinite mass, hence an a.s. infinite number of points. Somehow surprisingly (as I thought this was the point of the paper), the authors then switch to a truncated version of the process that always has a fixed number N of points. And whose density has the closed form

$p(x_1,\ldots,x_n) = \dfrac{1}{\pi^N} \prod_i \frac{1}{i!} \exp\{-|z_i|^2/2\}\prod_{i

It has an interestingly repulsive quality in that points cannot get close to one another. (It reminded me of the pinball sampler proposed by Kerrie Mengersen and myself at one of the Valencia meetings and not pursued since.) The conclusion (of this section) is anticlimactic, though,  in that it is known that this density also corresponds to the distribution of the eigenvalues of an Hermitian matrix with standardized complex Gaussian entries. The authors mentions that the fact that the support is the whole complex space Cn is a difficulty, although I do not see why.

The following sections of the paper move to the Ginibre process restricted to a compact and then to the truncated Ginibre process restricted to a compact, for which the authors develop corresponding simulation algorithms. There is however a drag in that the sequence of conditionals, while available in closed-form, cannot be simulated efficiently but rely on a uniform accept-reject instead. While I am certainly missing most of the points in the paper, I wonder if a Gibbs sampler would not be an interesting alternative given that the full (last) conditional is a Gaussian density…

### 3 Responses to “simulating determinantal processes”

1. Dan Simpson Says:

There’s a nifty paper (but very long) Jesper Møller and co wrote on this on the arXiv. He talked about it at the LGM in Iceland.

http://arxiv.org/abs/1205.4818

• Thanks, Dan!, it is on my pile of “to-read papers”, growing fast, too fast…

• Dan Simpson Says:

It’s actually easier to read than the 60 pages would suggest – it’s really only the first 20-odd pages that are necessary, the next 20 are computation (death by FFT) and examples (which aren’t super exciting but get the point across) and then the next 20 are technical appendices. It’s almost like 3 papers.

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