Statistical aspects of determinantal point processes Fr ed eric - - PowerPoint PPT Presentation

Introduction Definition Simulation Parametric models Inference

Statistical aspects of determinantal point processes

Fr´ ed´ eric Lavancier, Laboratoire de Math´ ematiques Jean Leray, Nantes (France) Joint work with Jesper Møller (Aalborg University, Danemark) and Ege Rubak (Aalborg University, Danemark).

Introduction Definition Simulation Parametric models Inference

1 Introduction 2 Definition, existence and basic properties 3 Simulation 4 Parametric stationary models

Parametric families of stationary DPPs Approximation of the eigen expansion Modelling of the eigen expansion

5 Inference

Introduction Definition Simulation Parametric models Inference

Type of data

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• Anemones

Hamster cells nuclei (dividing) Norwegian pines

Introduction Definition Simulation Parametric models Inference

Examples of models realisations

Realisations of Poisson point process versus Determinantal point process (DPP)

• Poisson

DPP DPP with stronger repulsion

Introduction Definition Simulation Parametric models Inference

Introduction

Determinantal point processes (DPP) form a class of

repulsive point processes.

Introduction Definition Simulation Parametric models Inference

Introduction

Determinantal point processes (DPP) form a class of

repulsive point processes.

They were introduced in their general form by O. Macchi

in 1975 to model fermions (i.e. particles with repulsion) in quantum mechanics.

Introduction Definition Simulation Parametric models Inference

Introduction

Determinantal point processes (DPP) form a class of

repulsive point processes.

They were introduced in their general form by O. Macchi

in 1975 to model fermions (i.e. particles with repulsion) in quantum mechanics.

Particular cases include the law of the eigenvalues of

certain random matrices (Gaussian Unitary Ensemble, Ginibre Ensemble,...)

Introduction Definition Simulation Parametric models Inference

Introduction

Determinantal point processes (DPP) form a class of

repulsive point processes.

They were introduced in their general form by O. Macchi

in 1975 to model fermions (i.e. particles with repulsion) in quantum mechanics.

Particular cases include the law of the eigenvalues of

certain random matrices (Gaussian Unitary Ensemble, Ginibre Ensemble,...)

Most theoretical studies have been published in the 2000’s.

Introduction Definition Simulation Parametric models Inference

Introduction

Determinantal point processes (DPP) form a class of

repulsive point processes.

They were introduced in their general form by O. Macchi

in 1975 to model fermions (i.e. particles with repulsion) in quantum mechanics.

Particular cases include the law of the eigenvalues of

certain random matrices (Gaussian Unitary Ensemble, Ginibre Ensemble,...)

Most theoretical studies have been published in the 2000’s. The statistical aspects have so far been largely

unexplored.

Introduction Definition Simulation Parametric models Inference

Statistical motivation

Do DPP’s constitute a tractable and flexible class of models for repulsive point processes?

Introduction Definition Simulation Parametric models Inference

Statistical motivation

Do DPP’s constitute a tractable and flexible class of models for repulsive point processes? − → Answer: YES.

Introduction Definition Simulation Parametric models Inference

Statistical motivation

Do DPP’s constitute a tractable and flexible class of models for repulsive point processes? − → Answer: YES. In fact:

DPP’s can be easily and quickly simulated. There are closed form expressions for the moments. There is a closed form expression for the density of a DPP

• n any bounded set.

Inference is feasible, including likelihood inference.

Introduction Definition Simulation Parametric models Inference

Statistical motivation

Do DPP’s constitute a tractable and flexible class of models for repulsive point processes? − → Answer: YES. In fact:

DPP’s can be easily and quickly simulated. There are closed form expressions for the moments. There is a closed form expression for the density of a DPP

• n any bounded set.

Inference is feasible, including likelihood inference.

These properties are unusual for Gibbs point processes which are commonly used to model inhibition (e.g. Strauss process).

Introduction Definition Simulation Parametric models Inference

1 Introduction 2 Definition, existence and basic properties 3 Simulation 4 Parametric stationary models

Parametric families of stationary DPPs Approximation of the eigen expansion Modelling of the eigen expansion

5 Inference

Introduction Definition Simulation Parametric models Inference

Notation

We view a spatial point process X on Rd as a random

locally finite subset of Rd.

Introduction Definition Simulation Parametric models Inference

Notation

We view a spatial point process X on Rd as a random

locally finite subset of Rd.

For any borel set B ⊆ Rd, XB = X ∩ B.

Introduction Definition Simulation Parametric models Inference

Notation

We view a spatial point process X on Rd as a random

locally finite subset of Rd.

For any borel set B ⊆ Rd, XB = X ∩ B. For any integer n > 0, denote ρ(n) the n’th order product

density function of X. Intuitively, ρ(n)(x1, . . . , xn) dx1 · · · dxn is the probability that for each i = 1, . . . , n, X has a point in a region around xi of volume dxi.

Introduction Definition Simulation Parametric models Inference

Notation

We view a spatial point process X on Rd as a random

locally finite subset of Rd.

For any borel set B ⊆ Rd, XB = X ∩ B. For any integer n > 0, denote ρ(n) the n’th order product

density function of X. Intuitively, ρ(n)(x1, . . . , xn) dx1 · · · dxn is the probability that for each i = 1, . . . , n, X has a point in a region around xi of volume dxi. In particular ρ = ρ(1) is the intensity function.

Introduction Definition Simulation Parametric models Inference

Definition of a determinantal point process

For any function C : Rd × Rd → C, denote [C](x1, . . . , xn) the n × n matrix with entries C(xi, xj). Ex : [C](x1) = C(x1, x1) [C](x1, x2) = C(x1, x1) C(x1, x2) C(x2, x1) C(x2, x2)

• .

Introduction Definition Simulation Parametric models Inference

Definition of a determinantal point process

For any function C : Rd × Rd → C, denote [C](x1, . . . , xn) the n × n matrix with entries C(xi, xj). Ex : [C](x1) = C(x1, x1) [C](x1, x2) = C(x1, x1) C(x1, x2) C(x2, x1) C(x2, x2)

• .

Definition X is a determinantal point process with kernel C, denoted X ∼ DPP(C), if its product density functions satisfy ρ(n)(x1, . . . , xn) = det[C](x1, . . . , xn), n = 1, 2, . . .

Introduction Definition Simulation Parametric models Inference

Definition of a determinantal point process

For any function C : Rd × Rd → C, denote [C](x1, . . . , xn) the n × n matrix with entries C(xi, xj). Ex : [C](x1) = C(x1, x1) [C](x1, x2) = C(x1, x1) C(x1, x2) C(x2, x1) C(x2, x2)

• .

Definition X is a determinantal point process with kernel C, denoted X ∼ DPP(C), if its product density functions satisfy ρ(n)(x1, . . . , xn) = det[C](x1, . . . , xn), n = 1, 2, . . . The Poisson process with intensity ρ(x) is the special case where C(x, x) = ρ(x) and C(x, y) = 0 if x = y.

Introduction Definition Simulation Parametric models Inference

Definition of a determinantal point process

For any function C : Rd × Rd → C, denote [C](x1, . . . , xn) the n × n matrix with entries C(xi, xj). Ex : [C](x1) = C(x1, x1) [C](x1, x2) = C(x1, x1) C(x1, x2) C(x2, x1) C(x2, x2)

• .

Definition X is a determinantal point process with kernel C, denoted X ∼ DPP(C), if its product density functions satisfy ρ(n)(x1, . . . , xn) = det[C](x1, . . . , xn), n = 1, 2, . . . The Poisson process with intensity ρ(x) is the special case where C(x, x) = ρ(x) and C(x, y) = 0 if x = y. For existence, conditions on the kernel C are mandatory, e.g. C must satisfy : for all x1, . . . , xn, det[C](x1, . . . , xn) ≥ 0.

Introduction Definition Simulation Parametric models Inference

First properties

From the definition, if C is continuous,

ρ(n)(x1, . . . , xn) ≈ 0 whenever xi ≈ xj for some i = j, = ⇒ the points of X repel each other.

Introduction Definition Simulation Parametric models Inference

First properties

From the definition, if C is continuous,

ρ(n)(x1, . . . , xn) ≈ 0 whenever xi ≈ xj for some i = j, = ⇒ the points of X repel each other.

The intensity of X is ρ(x) = C(x, x).

Introduction Definition Simulation Parametric models Inference

First properties

From the definition, if C is continuous,

ρ(n)(x1, . . . , xn) ≈ 0 whenever xi ≈ xj for some i = j, = ⇒ the points of X repel each other.

The intensity of X is ρ(x) = C(x, x). The pair correlation function is

g(x, y) := ρ(2)(x, y) ρ(x)ρ(y) = 1 − C(x, y)C(y, x) C(x, x)C(y, y)

Thus g ≤ 1 (i.e. repulsiveness) if C is Hermitian.

Introduction Definition Simulation Parametric models Inference

First properties

From the definition, if C is continuous,

ρ(n)(x1, . . . , xn) ≈ 0 whenever xi ≈ xj for some i = j, = ⇒ the points of X repel each other.

The intensity of X is ρ(x) = C(x, x). The pair correlation function is

g(x, y) := ρ(2)(x, y) ρ(x)ρ(y) = 1 − C(x, y)C(y, x) C(x, x)C(y, y)

Thus g ≤ 1 (i.e. repulsiveness) if C is Hermitian. If X ∼ DPP(C), then XB ∼ DPPB(CB)

Introduction Definition Simulation Parametric models Inference

First properties

From the definition, if C is continuous,

ρ(n)(x1, . . . , xn) ≈ 0 whenever xi ≈ xj for some i = j, = ⇒ the points of X repel each other.

The intensity of X is ρ(x) = C(x, x). The pair correlation function is

g(x, y) := ρ(2)(x, y) ρ(x)ρ(y) = 1 − C(x, y)C(y, x) C(x, x)C(y, y)

Thus g ≤ 1 (i.e. repulsiveness) if C is Hermitian. If X ∼ DPP(C), then XB ∼ DPPB(CB) Any smooth transformation or independent thinning of a

DPP is still a DPP with explicit given kernel.

Introduction Definition Simulation Parametric models Inference

First properties

From the definition, if C is continuous,

ρ(n)(x1, . . . , xn) ≈ 0 whenever xi ≈ xj for some i = j, = ⇒ the points of X repel each other.

The intensity of X is ρ(x) = C(x, x). The pair correlation function is

g(x, y) := ρ(2)(x, y) ρ(x)ρ(y) = 1 − C(x, y)C(y, x) C(x, x)C(y, y)

Thus g ≤ 1 (i.e. repulsiveness) if C is Hermitian. If X ∼ DPP(C), then XB ∼ DPPB(CB) Any smooth transformation or independent thinning of a

DPP is still a DPP with explicit given kernel.

Given a kernel C, there exists at most one DPP(C).

Introduction Definition Simulation Parametric models Inference

Existence

In all that follows we assume (C1) C is a continuous complex covariance function.

Introduction Definition Simulation Parametric models Inference

Existence

In all that follows we assume (C1) C is a continuous complex covariance function. By Mercer’s theorem, for any compact set S ⊂ Rd, C restricted to S × S, denoted CS, has a spectral representation, CS(x, y) =

∞

• k=1

λS

k φS k (x)φS k (y),

(x, y) ∈ S × S, where λS

k ≥ 0 and

• S φS

k (x)φS l (x) dx = 1{k=l}.

Introduction Definition Simulation Parametric models Inference

Existence

In all that follows we assume (C1) C is a continuous complex covariance function. By Mercer’s theorem, for any compact set S ⊂ Rd, C restricted to S × S, denoted CS, has a spectral representation, CS(x, y) =

∞

• k=1

λS

k φS k (x)φS k (y),

(x, y) ∈ S × S, where λS

k ≥ 0 and

• S φS

k (x)φS l (x) dx = 1{k=l}.

Theorem (Macchi, 1975) Under (C1), existence of DPP(C) is equivalent to : λS

k ≤ 1 for all compact S ⊂ Rd and all k.

Introduction Definition Simulation Parametric models Inference

Density on a compact set S

Let X ∼ DPP(C) and S ⊂ Rd be any compact set. Recall that CS(x, y) = ∞

k=1 λS k φS k (x)φS k (y).

Theorem (Macchi (1975)) Assuming λS

k < 1, for all k, then XS is absolutely continuous

with respect to the homogeneous Poisson process on S with unit intensity, and has density f({x1, . . . , xn}) = exp(|S| − D) det[ ˜ C](x1, . . . , xn), where D = − ∞

k=1 log(1 − λS k ) and ˜

C : S × S → C is given by ˜ C(x, y) =

∞

• k=1

λS

k

1 − λS

k

φS

k (x)φS k (y)

Introduction Definition Simulation Parametric models Inference

1 Introduction 2 Definition, existence and basic properties 3 Simulation 4 Parametric stationary models

Parametric families of stationary DPPs Approximation of the eigen expansion Modelling of the eigen expansion

5 Inference

Introduction Definition Simulation Parametric models Inference

Let X ∼ DPP(C). We want to simulate XS for S ⊂ Rd compact. Recall that XS ∼ DPP(CS) with CS(x, y) = ∞

k=1 λS k φS k (x)φS k (y).

Introduction Definition Simulation Parametric models Inference

Let X ∼ DPP(C). We want to simulate XS for S ⊂ Rd compact. Recall that XS ∼ DPP(CS) with CS(x, y) = ∞

k=1 λS k φS k (x)φS k (y).

Theorem (Hough et al. (2006)) For k ∈ N, let Bk be independent Bernoulli r.v. with mean λS

k .

Define K(x, y) =

∞

• k=1

BkφS

k (x)φS k (y),

(x, y) ∈ S × S. Then DPP(CS) d = DPP(K).

Introduction Definition Simulation Parametric models Inference

So simulating XS is equivalent to simulate DPP(K) with K(x, y) =

∞

• k=1

BkφS

k (x)φS k (y),

(x, y) ∈ S × S.

Introduction Definition Simulation Parametric models Inference

So simulating XS is equivalent to simulate DPP(K) with K(x, y) =

∞

• k=1

BkφS

k (x)φS k (y),

(x, y) ∈ S × S. Note that M := sup{k ≥ 0 : Bk = 0} is a.s. finite, since λS

k =

• S C(x, x) dx < ∞.

Introduction Definition Simulation Parametric models Inference

So simulating XS is equivalent to simulate DPP(K) with K(x, y) =

∞

• k=1

BkφS

k (x)φS k (y),

(x, y) ∈ S × S. Note that M := sup{k ≥ 0 : Bk = 0} is a.s. finite, since λS

k =

• S C(x, x) dx < ∞.

1 Simulate a realization M = m (by the inversion method).

Introduction Definition Simulation Parametric models Inference

So simulating XS is equivalent to simulate DPP(K) with K(x, y) =

∞

• k=1

BkφS

k (x)φS k (y),

(x, y) ∈ S × S. Note that M := sup{k ≥ 0 : Bk = 0} is a.s. finite, since λS

k =

• S C(x, x) dx < ∞.

1 Simulate a realization M = m (by the inversion method). 2 Generate the Bernoulli variables B1, . . . , Bm−1 (these are

independent of {M = m}).

Introduction Definition Simulation Parametric models Inference

So simulating XS is equivalent to simulate DPP(K) with K(x, y) =

∞

• k=1

BkφS

k (x)φS k (y),

(x, y) ∈ S × S. Note that M := sup{k ≥ 0 : Bk = 0} is a.s. finite, since λS

k =

• S C(x, x) dx < ∞.

1 Simulate a realization M = m (by the inversion method). 2 Generate the Bernoulli variables B1, . . . , Bm−1 (these are

independent of {M = m}).

3 simulate the point process DPP(K) given B1, . . . , BM and

M = m.

Introduction Definition Simulation Parametric models Inference

So simulating XS is equivalent to simulate DPP(K) with K(x, y) =

∞

• k=1

BkφS

k (x)φS k (y),

(x, y) ∈ S × S. Note that M := sup{k ≥ 0 : Bk = 0} is a.s. finite, since λS

k =

• S C(x, x) dx < ∞.

1 Simulate a realization M = m (by the inversion method). 2 Generate the Bernoulli variables B1, . . . , Bm−1 (these are

independent of {M = m}).

3 simulate the point process DPP(K) given B1, . . . , BM and

M = m. In step 3, the kernel K becomes a projection kernel, and w.l.o.g. K(x, y) =

n

• k=1

φS

k (x)φS k (y)

where n = #{1 ≤ k ≤ M : Bk = 1}.

Introduction Definition Simulation Parametric models Inference

Simulation of determinantal projection processes

The point process DPP(K) has a.s. n points (X1, . . . , Xn) that can be simulated by the following Gram-Schmidt procedure, where K(x, y) =

n

• k=1

φS

k (x)φS k (y) = v(y)∗v(x),

v(x) = (φS

1 (x), . . . , φS n(x))T .

Introduction Definition Simulation Parametric models Inference

Simulation of determinantal projection processes

The point process DPP(K) has a.s. n points (X1, . . . , Xn) that can be simulated by the following Gram-Schmidt procedure, where K(x, y) =

n

• k=1

φS

k (x)φS k (y) = v(y)∗v(x),

v(x) = (φS

1 (x), . . . , φS n(x))T .

sample Xn from the density pn(x) = K(x, x)/n = v(x)2/n; for i = (n − 1) to 1 do set Hi = spanC{v(Xn), . . . , v(Xi+1)} sample Xi from the density (given Xi+1, . . . , Xn) : pi(x) = PH⊥

i v(x)2/i

where PH⊥

i denotes the projection onto H⊥

i

Introduction Definition Simulation Parametric models Inference

Simulation of determinantal projection processes

The point process DPP(K) has a.s. n points (X1, . . . , Xn) that can be simulated by the following Gram-Schmidt procedure, where K(x, y) =

n

• k=1

φS

k (x)φS k (y) = v(y)∗v(x),

v(x) = (φS

1 (x), . . . , φS n(x))T .

sample Xn from the density pn(x) = K(x, x)/n = v(x)2/n; for i = (n − 1) to 1 do set Hi = spanC{v(Xn), . . . , v(Xi+1)} sample Xi from the density (given Xi+1, . . . , Xn) : pi(x) = PH⊥

i v(x)2/i

where PH⊥

i denotes the projection onto H⊥

i

Theorem {X1, . . . , Xn} generated as above has distribution DPP(K).

Introduction Definition Simulation Parametric models Inference

Simulation of determinantal projection processes

The above algorithm can be implemented specifically as follows: sample Xn from the density pn(x) = v(x)2/n; set e1 = v(Xn)/v(Xn); for i = (n − 1) to 1 do sample Xi from the density (given Xi+1, . . . , Xn) : pi(x) = 1 i  v(x)2 −

n−i

• j=1

|e∗

jv(x)|2

  , x ∈ S set wi = v(Xi) − n−i

j=1

• e∗

jv(Xi)

• ej, en−i+1 = wi/wi

Introduction Definition Simulation Parametric models Inference

Illustration of simulation algorithm

Example : Consider the unit box S = [−1/2, 1/2]2 and : φk(x) = e2πik·x, k ∈ Z2, x ∈ S. For some λS

k ∈ [0, 1] and Bernoulli variables Bk’s with mean λk,

DPP ∞

• k=1

λS

k e2πik·(x−y)

• d

= DPP ∞

• k=1

Bk e2πik·(x−y)

• After simulation of the Bk’s (step 1 and 2), the simulation

reduces to simulate DPP(K) where K is a projection kernel, namely for a set of indices k1, . . . , kn in Z2: K(x, y) =

n

• j=1

e2πikj·(x−y) DPP(K) is homogeneous (ρ(1) = n) and has a.s. n points on S.

Introduction Definition Simulation Parametric models Inference

Illustration of simulation algorithm

Step 1. Since pn(x) = 1, the first point is sampled uniformly on S

Introduction Definition Simulation Parametric models Inference

Illustration of simulation algorithm

Step 1. Since pn(x) = 1, the first point is sampled uniformly on S Step 2. The next point is sampled w.r.t. the following density:

0.2 0.4 0.6 0.8 1

Introduction Definition Simulation Parametric models Inference

Illustration of simulation algorithm

Step 3. The next point is sampled w.r.t the following density :

0.2 0.4 0.6 0.8 1

Introduction Definition Simulation Parametric models Inference

Illustration of simulation algorithm

etc.

0.2 0.4 0.6 0.8 1

Introduction Definition Simulation Parametric models Inference

Illustration of simulation algorithm

etc.

0.1 0.2 0.3 0.4 0.5 0.6

Introduction Definition Simulation Parametric models Inference

Illustration of simulation algorithm

etc.

0.05 0.1 0.15

Introduction Definition Simulation Parametric models Inference

1 Introduction 2 Definition, existence and basic properties 3 Simulation 4 Parametric stationary models

Parametric families of stationary DPPs Approximation of the eigen expansion Modelling of the eigen expansion

5 Inference

Introduction Definition Simulation Parametric models Inference

1 Introduction 2 Definition, existence and basic properties 3 Simulation 4 Parametric stationary models

Parametric families of stationary DPPs Approximation of the eigen expansion Modelling of the eigen expansion

5 Inference

Introduction Definition Simulation Parametric models Inference

Stationary models

Consider a stationary kernel C(x, y) = C0(x − y), x, y ∈ Rd.

Introduction Definition Simulation Parametric models Inference

Stationary models

Consider a stationary kernel C(x, y) = C0(x − y), x, y ∈ Rd. Recall (C1): C0 is a continuous covariance function. Moreover, if C0 ∈ L2(Rd) we can define its Fourier transform ϕ(x) =

• C0(t)e−2πix·t dt,

x ∈ Rd.

Introduction Definition Simulation Parametric models Inference

Stationary models

Consider a stationary kernel C(x, y) = C0(x − y), x, y ∈ Rd. Recall (C1): C0 is a continuous covariance function. Moreover, if C0 ∈ L2(Rd) we can define its Fourier transform ϕ(x) =

• C0(t)e−2πix·t dt,

x ∈ Rd. Theorem Under (C1), if C0 ∈ L2(Rd), then existence of DPP(C0) is equivalent to ϕ ≤ 1.

Introduction Definition Simulation Parametric models Inference

Stationary models

Consider a stationary kernel C(x, y) = C0(x − y), x, y ∈ Rd. Recall (C1): C0 is a continuous covariance function. Moreover, if C0 ∈ L2(Rd) we can define its Fourier transform ϕ(x) =

• C0(t)e−2πix·t dt,

x ∈ Rd. Theorem Under (C1), if C0 ∈ L2(Rd), then existence of DPP(C0) is equivalent to ϕ ≤ 1. To construct parametric families of DPP : Consider parametric families of C0 and rescale so that ϕ ≤ 1. → This will induce restriction on the parameter space.

Introduction Definition Simulation Parametric models Inference

Several parametric families of covariance function are available, with closed form expressions for their Fourier transform.

Introduction Definition Simulation Parametric models Inference

Several parametric families of covariance function are available, with closed form expressions for their Fourier transform.

For d = 2, the circular covariance function with range α is given by

C0(x) = ρ 2 π

• arccos(x/α) − x/α
• 1 − (x/α)2
• 1x<α.

DPP(C0) exists iff ϕ ≤ 1 ⇔ ρα2 ≤ 4/π. ⇒ Tradeoff between the intensity ρ and the range of repulsion α.

Introduction Definition Simulation Parametric models Inference

Several parametric families of covariance function are available, with closed form expressions for their Fourier transform.

For d = 2, the circular covariance function with range α is given by

C0(x) = ρ 2 π

• arccos(x/α) − x/α
• 1 − (x/α)2
• 1x<α.

DPP(C0) exists iff ϕ ≤ 1 ⇔ ρα2 ≤ 4/π. ⇒ Tradeoff between the intensity ρ and the range of repulsion α.

Whittle-Mat´

ern (includes Exponential and Gaussian) : C0(x) = ρ 21−ν Γ(ν) x/ανKν(x/α), x ∈ Rd, DPP(C0) exists iff ρ ≤

Γ(ν) Γ(ν+d/2)(2√πα)d

Introduction Definition Simulation Parametric models Inference

Several parametric families of covariance function are available, with closed form expressions for their Fourier transform.

For d = 2, the circular covariance function with range α is given by

C0(x) = ρ 2 π

• arccos(x/α) − x/α
• 1 − (x/α)2
• 1x<α.

DPP(C0) exists iff ϕ ≤ 1 ⇔ ρα2 ≤ 4/π. ⇒ Tradeoff between the intensity ρ and the range of repulsion α.

Whittle-Mat´

ern (includes Exponential and Gaussian) : C0(x) = ρ 21−ν Γ(ν) x/ανKν(x/α), x ∈ Rd, DPP(C0) exists iff ρ ≤

Γ(ν) Γ(ν+d/2)(2√πα)d Generalized Cauchy

C0(x) = ρ (1 + x/α2)ν+d/2 , x ∈ Rd. DPP(C0) exists iff ρ ≤

Γ(ν+d/2) Γ(ν)(√πα)d

Introduction Definition Simulation Parametric models Inference

Pair correlation functions of DPP(C0) for previous models when the scaling parameter α is chosen such that the range of corr. ≈ 1: In blue : C0 is the circular covariance function. In red : C0 is Whittle-Mat´ ern, for different values of ν In green : C0 is generalized Cauchy, for different values of ν

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 r = |x − y| g(r)

Introduction Definition Simulation Parametric models Inference

1 Introduction 2 Definition, existence and basic properties 3 Simulation 4 Parametric stationary models

Parametric families of stationary DPPs Approximation of the eigen expansion Modelling of the eigen expansion

5 Inference

Introduction Definition Simulation Parametric models Inference

Approximation of stationary DPP’s models

Consider a stationary kernel C0 and X ∼ DPP(C0).

• The simulation and the density of XS requires the expansion

CS(x, y) = C0(y − x) =

∞

• k=1

λS

k φS k (x)φS k (y),

(x, y) ∈ S × S, but in general λS

k and φS k are not expressible on closed form.

Introduction Definition Simulation Parametric models Inference

Approximation of stationary DPP’s models

Consider a stationary kernel C0 and X ∼ DPP(C0).

• The simulation and the density of XS requires the expansion

CS(x, y) = C0(y − x) =

∞

• k=1

λS

k φS k (x)φS k (y),

(x, y) ∈ S × S, but in general λS

k and φS k are not expressible on closed form.

• Consider w.l.g. the unit box S = [− 1

2, 1 2]d and the Fourier expansion

C0(y − x) =

• k∈Zd

cke2πik·(y−x), y − x ∈ S.

Introduction Definition Simulation Parametric models Inference

Approximation of stationary DPP’s models

Consider a stationary kernel C0 and X ∼ DPP(C0).

• The simulation and the density of XS requires the expansion

CS(x, y) = C0(y − x) =

∞

• k=1

λS

k φS k (x)φS k (y),

(x, y) ∈ S × S, but in general λS

k and φS k are not expressible on closed form.

• Consider w.l.g. the unit box S = [− 1

2, 1 2]d and the Fourier expansion

C0(y − x) =

• k∈Zd

cke2πik·(y−x), y − x ∈ S. The Fourier coefficients are ck =

• S

C0(u)e−2πik·u du ≈

• Rd C0(u)e−2πik·u du = ϕ(k)

which is a good approximation if C0(u) ≈ 0 for |u| > 1

2.

Introduction Definition Simulation Parametric models Inference

Approximation of stationary DPP’s models

Consider a stationary kernel C0 and X ∼ DPP(C0).

• The simulation and the density of XS requires the expansion

CS(x, y) = C0(y − x) =

∞

• k=1

λS

k φS k (x)φS k (y),

(x, y) ∈ S × S, but in general λS

k and φS k are not expressible on closed form.

• Consider w.l.g. the unit box S = [− 1

2, 1 2]d and the Fourier expansion

C0(y − x) =

• k∈Zd

cke2πik·(y−x), y − x ∈ S. The Fourier coefficients are ck =

• S

C0(u)e−2πik·u du ≈

• Rd C0(u)e−2πik·u du = ϕ(k)

which is a good approximation if C0(u) ≈ 0 for |u| > 1

2.

• Example: For the circular covariance, this is true whenever ρ|S| > 5.

Introduction Definition Simulation Parametric models Inference

Approximation of stationary models

Our approximation of DPP(C0) on S is then DPP(Capp,0) with Capp,0(x) =

• k∈Zd

ϕ(k)e2πix·k, where ϕ is the Fourier transform of C0. This approximation allows us

to simulate DPP(C0) on S, by simulating DPP(Capp,0) to compute the (approximated) density of DPP(C0) on S,

as the density of DPP(Capp,0).

Introduction Definition Simulation Parametric models Inference

1 Introduction 2 Definition, existence and basic properties 3 Simulation 4 Parametric stationary models

Parametric families of stationary DPPs Approximation of the eigen expansion Modelling of the eigen expansion

5 Inference

Introduction Definition Simulation Parametric models Inference

Modelling approach based on spectral densities

Idea: instead of modelling C0, model λS

k and φS k in

C0(y − x) =

∞

• k=1

λS

k φS k (x)φS k (y)

Introduction Definition Simulation Parametric models Inference

Modelling approach based on spectral densities

Idea: instead of modelling C0, model λS

k and φS k in

C0(y − x) =

∞

• k=1

λS

k φS k (x)φS k (y)

Following the previous approximation :

Choose the Fourier basis on S : φk(x) = e−2πik·x/|S|. Choose λk = ϕ(k), where ϕ is a spectral density with ϕ ≤ 1.

Introduction Definition Simulation Parametric models Inference

Modelling approach based on spectral densities

Idea: instead of modelling C0, model λS

k and φS k in

C0(y − x) =

∞

• k=1

λS

k φS k (x)φS k (y)

Following the previous approximation :

Choose the Fourier basis on S : φk(x) = e−2πik·x/|S|. Choose λk = ϕ(k), where ϕ is a spectral density with ϕ ≤ 1. This is all we need for having a well-defined DPP.

Introduction Definition Simulation Parametric models Inference

Modelling approach based on spectral densities

Idea: instead of modelling C0, model λS

k and φS k in

C0(y − x) =

∞

• k=1

λS

k φS k (x)φS k (y)

Following the previous approximation :

Choose the Fourier basis on S : φk(x) = e−2πik·x/|S|. Choose λk = ϕ(k), where ϕ is a spectral density with ϕ ≤ 1. This is all we need for having a well-defined DPP. The simulation then relies on the exact λk’s and φk’s.

Introduction Definition Simulation Parametric models Inference

Modelling approach based on spectral densities

Idea: instead of modelling C0, model λS

k and φS k in

C0(y − x) =

∞

• k=1

λS

k φS k (x)φS k (y)

Following the previous approximation :

Choose the Fourier basis on S : φk(x) = e−2πik·x/|S|. Choose λk = ϕ(k), where ϕ is a spectral density with ϕ ≤ 1. This is all we need for having a well-defined DPP. The simulation then relies on the exact λk’s and φk’s. The density of probability is given exactly (up to the series

truncature).

Introduction Definition Simulation Parametric models Inference

Modelling approach based on spectral densities

Idea: instead of modelling C0, model λS

k and φS k in

C0(y − x) =

∞

• k=1

λS

k φS k (x)φS k (y)

Following the previous approximation :

Choose the Fourier basis on S : φk(x) = e−2πik·x/|S|. Choose λk = ϕ(k), where ϕ is a spectral density with ϕ ≤ 1. This is all we need for having a well-defined DPP. The simulation then relies on the exact λk’s and φk’s. The density of probability is given exactly (up to the series

truncature).

C0 can be approximated by F−1ϕ : if a closed form expression is

available, this gives approximated expressions for g, etc.

Introduction Definition Simulation Parametric models Inference

Modelling approach based on spectral densities

Idea: instead of modelling C0, model λS

k and φS k in

C0(y − x) =

∞

• k=1

λS

k φS k (x)φS k (y)

Following the previous approximation :

Choose the Fourier basis on S : φk(x) = e−2πik·x/|S|. Choose λk = ϕ(k), where ϕ is a spectral density with ϕ ≤ 1. This is all we need for having a well-defined DPP. The simulation then relies on the exact λk’s and φk’s. The density of probability is given exactly (up to the series

truncature).

C0 can be approximated by F−1ϕ : if a closed form expression is

available, this gives approximated expressions for g, etc. Parametric models can be constructed by specifying a parametric class of spectral densities ϕθ : Rd → [0, 1]

Introduction Definition Simulation Parametric models Inference

Example : power exponential spectral model

ϕρ,ν,α(x) = ρΓ(d/2 + 1)ναd dπd/2Γ(d/ν) exp (−αxν) with ρ > 0, ν > 0, 0 < α ≤ αmax(ρ, ν)

5 10 15 0.0 0.2 0.4 0.6 0.8 1.0

ν = 1 ν = 2 (Gauss) ν = 3 ν = 5 ν = 10 ν = ∞

0.00 0.05 0.10 0.15 0.20 0.0 0.2 0.4 0.6 0.8 1.0

ν = 1 ν = 2 (Gauss) ν = 3 ν = 5 ν = 10 ν = ∞

Spectral densities Approximated pair correlation functions

Introduction Definition Simulation Parametric models Inference

1 Introduction 2 Definition, existence and basic properties 3 Simulation 4 Parametric stationary models

Parametric families of stationary DPPs Approximation of the eigen expansion Modelling of the eigen expansion

5 Inference

Introduction Definition Simulation Parametric models Inference

Consider a stationary and isotropic parametric DPP(C), i.e. C(x, y) = C0(x − y) = ρRα(x − y), with Rα(0) = 1. The first moments are easily deduced :

The intensity is ρ The pair correlation function is

g(x, y) = g0(x − y) = 1 − R2

α(x − y).

Ripley’s K-function is easily expressible in terms of Rα as

(for d = 2) Kα(r) := 2π r tg0(t) dt = πr2 − 2π r t|Rα(t)|2dt.

Introduction Definition Simulation Parametric models Inference

Inference

The estimation can be conducted as follows

1 Estimate ρ by the mean number of points, i.e.

ˆ ρ = #{obs. points} area of obs. window

Introduction Definition Simulation Parametric models Inference

Inference

The estimation can be conducted as follows

1 Estimate ρ by the mean number of points, i.e.

ˆ ρ = #{obs. points} area of obs. window

2 Estimate α

either by minimum contrast estimator (MCE) : ˆ α = argminα rmax

• K(r) −
• Kα(r)
• 2

dr

• r by maximum likelihood estimator : given ˆ

ρ, the likelihood is deduced from the kernel approximation.

Introduction Definition Simulation Parametric models Inference

Two model examples

In the following we will consider two different model examples :

An exponential model with

ρ = 200 and α = 0.014 : C0(x) = ρ exp(−x/α)

A Gaussian model with

ρ = 200 and α = 0.02 : C0(x) = ρ exp(−x/α2)

0.00 0.02 0.04 0.06 0.08 0.10 0.0 0.2 0.4 0.6 0.8 1.0

• Exponential model

Gaussian model

− Solid lines : theoretical pair correlation function

• Circles : pair correlation from the approximated kernel

Introduction Definition Simulation Parametric models Inference

Samples from the Gaussian model on [0, 1]2 :

• Samples from the Exponential model on [0, 1]2 :

Introduction Definition Simulation Parametric models Inference

Estimation of α from 200 realisations

• MCE

MLE 0.005 0.015 0.025 0.035 MCE MLE 0.000 0.010 0.020 0.030

Gaussian model Exponential model

Introduction Definition Simulation Parametric models Inference

Example: 134 Norwegian pine trees observed in a 56 × 38 m region

• ●
• ●
• Møller and Waagpetersen (2004): a five parameter multiscale

process is fitted using elaborate MCMC MLE methods. Here we fit a more parsimonious DPP models.

Introduction Definition Simulation Parametric models Inference

First, a parametric model is assumed for C0,

either Whittle-Mat´

ern model; or generalized Cauchy model; or Gaussian model (the limit of both).

Gaussian model: the best fit, but plots of summary

statistics indicate a lack of fit.

Introduction Definition Simulation Parametric models Inference

First, a parametric model is assumed for C0,

either Whittle-Mat´

ern model; or generalized Cauchy model; or Gaussian model (the limit of both).

Gaussian model: the best fit, but plots of summary

statistics indicate a lack of fit. Second, a parametric model is assumed for the eigenvalues, through ϕ,

power exponential spectral model. A much better fit, with ˆ

ρ = 0.063, ˆ ν = 10, ˆ α = 6.36 i.e. ˆ ϕ is close to the “most repulsive possible stationary DPP” for which ϕ(x) =

• 1

if |x|2 < ρ/π

• therwise.

.

Introduction Definition Simulation Parametric models Inference

2 4 6 8 −1.2 −0.8 −0.4 0.0

Gauss Power exp. Data

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 0.0 0.2 0.4 0.6 0.8

Gauss Power exp. Data

0.0 0.5 1.0 1.5 2.0 2.5 0.0 0.2 0.4 0.6 0.8

Gauss Power exp. Data

0.0 0.5 1.0 1.5 2.0 2.5 1 2 3 4

Gauss Power exp. Data

Clockwise from top left: L(r) − r; G(r); F(r); J(r). Simulated 2.5% and 97.5% envelopes are based on 4000 realizations of the fitted models.

Introduction Definition Simulation Parametric models Inference

Conclusion

DPP provides some flexible parametric models of repulsive point processes. Furthermore DPP possess the following appealing properties :

They can be easily simulated There are closed form expressions for the moments of a

DPP

There are closed form expression for the density of a DPP

• n any bounded set

Inference is feasible, including likelihood inference.

⇒ Promising alternative to repulsive Gibbs point processes.

Introduction Definition Simulation Parametric models Inference

References

Hough, J. B., M. Krishnapur, Y. Peres, and B. Vir` ag (2006). Determinantal processes and independence. Probability Surveys 3, 206–229. Macchi, O. (1975). The coincidence approach to stochastic point processes. Advances in Applied Probability 7, 83–122. McCullagh, P. and J. Møller (2006). The permanental process. Advances in Applied Probability 38, 873–888. Scardicchio, A., C. Zachary, and S. Torquato (2009). Statistical properties of determinantal point processes in high-dimensional Euclidean spaces. Physical Review E 79(4). Shirai, T. and Y. Takahashi (2003). Random point fields associated with certain Fredholm determinants. I. Fermion, Poisson and boson point processes. Journal of Functional Analysis 2, 414–463. Soshnikov, A. (2000). Determinantal random point fields. Russian Math. Surveys 55, 923–975.