scholarly journals Brownian Loops, Layering Fields and Imaginary Gaussian Multiplicative Chaos

2021 ◽  
Vol 381 (3) ◽  
pp. 889-945
Author(s):  
Federico Camia ◽  
Alberto Gandolfi ◽  
Giovanni Peccati ◽  
Tulasi Ram Reddy

AbstractWe study fields reminiscent of vertex operators built from the Brownian loop soup in the limit as the loop soup intensity tends to infinity. More precisely, following Camia et al. (Nucl Phys B 902:483–507, 2016), we take a (massless or massive) Brownian loop soup in a planar domain and assign a random sign to each loop. We then consider random fields defined by taking, at every point of the domain, the exponential of a purely imaginary constant times the sum of the signs associated to the loops that wind around that point. For domains conformally equivalent to a disk, the sum diverges logarithmically due to the small loops, but we show that a suitable renormalization procedure allows to define the fields in an appropriate Sobolev space. Subsequently, we let the intensity of the loop soup tend to infinity and prove that these vertex-like fields tend to a conformally covariant random field which can be expressed as an explicit functional of the imaginary Gaussian multiplicative chaos with covariance kernel given by the Brownian loop measure. Besides using properties of the Brownian loop soup and the Brownian loop measure, a main tool in our analysis is an explicit Wiener–Itô chaos expansion of linear functionals of vertex-like fields. Our methods apply to other variants of the model in which, for example, Brownian loops are replaced by disks.

2019 ◽  
Vol 21 (10) ◽  
pp. 3225-3253 ◽  
Author(s):  
Wei Qian ◽  
Wendelin Werner

1990 ◽  
Vol 119 ◽  
pp. 93-106 ◽  
Author(s):  
Ke-Seung Lee

The purpose of this paper is to investigate way of dependency of Gaussian random fields X(D) indexed by a domain D in d-dimensional Euclidean space Rd. Our main tool is variational calculus, where the boundary of a domain varies and deforms and we appeal to the white noise analysis. We therefore assume that X(D) is expressed white noise integral of the form(0.1) X(D) = X(D, W)=∫D F(D, u)W(u)du,where W is the Rd-parameter white noise and the kernel F(D, u) is a square integrable function over Rd, and where D is a bounded domain with smooth boundary.


2016 ◽  
Vol 902 ◽  
pp. 483-507 ◽  
Author(s):  
Federico Camia ◽  
Alberto Gandolfi ◽  
Matthew Kleban

2004 ◽  
Vol 128 (4) ◽  
pp. 565-588 ◽  
Author(s):  
Gregory F. Lawler ◽  
Wendelin Werner

2019 ◽  
Vol 175 (5) ◽  
pp. 987-1005 ◽  
Author(s):  
Yong Han ◽  
Yuefei Wang ◽  
Michel Zinsmeister

2013 ◽  
Vol 150 (6) ◽  
pp. 1030-1062 ◽  
Author(s):  
Laurence S. Field ◽  
Gregory F. Lawler

2020 ◽  
Vol 2020 (7) ◽  
Author(s):  
Federico Camia ◽  
Valentino F. Foit ◽  
Alberto Gandolfi ◽  
Matthew Kleban

2002 ◽  
Vol 7 (1) ◽  
pp. 31-42
Author(s):  
J. Šaltytė ◽  
K. Dučinskas

The Bayesian classification rule used for the classification of the observations of the (second-order) stationary Gaussian random fields with different means and common factorised covariance matrices is investigated. The influence of the observed data augmentation to the Bayesian risk is examined for three different nonlinear widely applicable spatial correlation models. The explicit expression of the Bayesian risk for the classification of augmented data is derived. Numerical comparison of these models by the variability of Bayesian risk in case of the first-order neighbourhood scheme is performed.


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