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[PDF] Top 20 Adaptive estimating function inference for non-stationary determinantal point processes

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Adaptive estimating function inference for non-stationary determinantal point processes

Adaptive estimating function inference for non-stationary determinantal point processes

... second-order estimating functions, a common approach [ 5 , 20 ] is to restrict the random sum to pairs of R-close points for some user-specified R ą ...available for the choice of R. Moreover, it is ... Voir le document complet

36

Asymptotic approximation of the likelihood of stationary determinantal point processes

Asymptotic approximation of the likelihood of stationary determinantal point processes

... computed for stationary parametric families of DPPs based on correlation functions with a known Fourier transform, as the classical ones presented in Table 1 ...works for rectangular windows, our ... Voir le document complet

33

Contribution to the modelling and the parametric estimation of determinantal point processes

Contribution to the modelling and the parametric estimation of determinantal point processes

... of determinantal point processes (DPPs) and their parametric ...These processes are known to be well adapted to inhibitive point patterns, where the points tend to repel each ... Voir le document complet

119

Brillinger mixing of determinantal point processes and statistical applications

Brillinger mixing of determinantal point processes and statistical applications

... correlation function of a DPP, which is a new result presented in Section ...useful for many other applications, see for instance [12], [17] and ...estimators for parametric ... Voir le document complet

25

On a few statistical applications of determinantal point processes

On a few statistical applications of determinantal point processes

... of stationary DPPs. Other contrast estimating functions are possible, where g is replaced by another characteristic of the point process, see Section 5 for an example with the J ...likelihood ... Voir le document complet

24

Determinantal point process models and statistical inference : Extended version

Determinantal point process models and statistical inference : Extended version

... models for DPPs and discussed to which degree they can model ...a function, viz. the kernel (or covariance function) C, which as illustrated in our examples of applications can be chosen in many ... Voir le document complet

62

Quantifying repulsiveness of determinantal point processes

Quantifying repulsiveness of determinantal point processes

... repulsive point patterns. For instance, as deduced from Section 3, stationary DPPs can not involve a hardcore distance be- tween points, contrary to the Mat´ern’s hardcore point ... Voir le document complet

31

Contrast estimation for parametric stationary determinantal point processes

Contrast estimation for parametric stationary determinantal point processes

... estimation for parametric stationary determinan- tal point ...These processes form a useful class of models for repulsive (or regular, or inhibitive) point patterns and are ... Voir le document complet

30

Standard and robust intensity parameter estimation for stationary determinantal point processes

Standard and robust intensity parameter estimation for stationary determinantal point processes

... Repulsive point processes; Statistical inference; Robust statistics; Sample quantiles; Brillinger ...Spatial point patterns are datasets containing the random locations of some event of ... Voir le document complet

22

Necessary and sufficient conditions for the existence of $\alpha$-determinantal processes

Necessary and sufficient conditions for the existence of $\alpha$-determinantal processes

... compactly-supported non-negative function on E, K[1 − e −f ] stands for √ 1 − e −f K √ 1 − e −f , I is the identity operator on L 2 (E, λ) and Det is the Fredholm de- ...correlation function ... Voir le document complet

21

Analysis of stationary and non-stationary long memory processes : estimation, applications and forecast

Analysis of stationary and non-stationary long memory processes : estimation, applications and forecast

... practical point of view, before implementing a fractional process on real data, it is warmly recommended to carry out a statistical test to show evidence of persistence in the ...useful for testing ... Voir le document complet

202

Efficient Policies for Stationary Possibilistic Markov  Decision Processes

Efficient Policies for Stationary Possibilistic Markov Decision Processes

... paradigm for sequential decision making under uncertainty is the one of expected utility-based Markov Decision Processes (MDP) [11, 2], which assumes that the uncertain effects of actions can be represented ... Voir le document complet

11

Statistical Inference for Oscillation Processes

Statistical Inference for Oscillation Processes

... increments. For this more general model we have estimated the unobserved phases by a computationally efficient Rao- Blackwellized Particle Smoother (RBPS) which allows for simultaneous estimation of the ... Voir le document complet

30

A determinantal point process for column subset selection

A determinantal point process for column subset selection

... To minimize approximation error, the subspace spanned by the selected columns should be as large as possible. Simultaneously, the number of selected columns should be as small as possible, so that intuitively, diversity ... Voir le document complet

50

Hawkes point processes based inference applied to seismic data analysis

Hawkes point processes based inference applied to seismic data analysis

... Theorem 3. Let {𝑡 1 , 𝑡 2 , . . . } be the arrival times of a point process 𝑁 . We define the cumulative inten- sity Λ as Λ(𝑡) = ∫︀ 0 𝑡 𝜆 * (𝑠) d𝑠. The residuals sequence {𝜏 1 , 𝜏 2 , . . . } = {Λ(𝑡 1 ), Λ(𝑡 2 ), ... Voir le document complet

7

Rigidity of Determinantal Point Processes with the Airy, the Bessel and the Gamma Kernel

Rigidity of Determinantal Point Processes with the Airy, the Bessel and the Gamma Kernel

... A point process is said to be rigid if for any bounded do- main in the phase space, the number of particles in the domain is almost surely determined by the restriction of the configuration to the comple- ... Voir le document complet

11

Residuals and goodness-of-fit tests for stationary marked Gibbs point processes

Residuals and goodness-of-fit tests for stationary marked Gibbs point processes

... done for point pro- cesses admitting a conditional density with respect to the Poisson ...These point processes correspond to the Gibbs ...basis for defining the class of h −residuals ... Voir le document complet

41

Rare Events for Stationary Processes

Rare Events for Stationary Processes

... Unité de recherche INRIA Lorraine, Technopôle de Nancy-Brabois, Campus scientifique, 615 rue du Jardin Botanique, BP 101, 54600 VILLERS LÈS NANCY Unité de recherche INRIA Rennes, Irisa, [r] ... Voir le document complet

39

Graph sampling with determinantal processes

Graph sampling with determinantal processes

... I. I NTRODUCTION Graphs are a central modelling tool for network-structured data. Data on a graph, called graph signals [1], such as indi- vidual hobbies in social networks, blood flow of brain regions in neuronal ... Voir le document complet

6

Estimating the galaxy two-point correlation function using a split random catalog

Estimating the galaxy two-point correlation function using a split random catalog

... correlation function of the galaxy distribution is a key cosmological observable that allows us to constrain the dynamical and geometrical state of our ...correlation function we need to know both the ... Voir le document complet

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