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We extend results of Zeitouni-Zelditch on large deviations principles for zeros of Gaussian random polynomials $s$ in one complex variable to certain non-Gaussian ensembles that we call $P(\phi)_2$ random polynomials. The probability…

Probability · Mathematics 2015-05-20 Renjie Feng , Steve Zelditch

Introducing inequality constraints in Gaussian process (GP) models can lead to more realistic uncertainties in learning a great variety of real-world problems. We consider the finite-dimensional Gaussian approach from Maatouk and Bay (2017)…

Machine Learning · Statistics 2021-11-04 Andrés F. López-Lopera , François Bachoc , Nicolas Durrande , Olivier Roustant

The pairing Hamiltonian constitutes an important approximation in many- body systems, it is exactly soluble and quantum integrable. On the other hand, the continuum single particle level density (CSPLD) contains information about the…

Nuclear Theory · Physics 2012-04-13 R. Id Betan

We study the density of complex critical points of a real random SO(m+1) polynomial in m variables. In a previous paper [Mac09], the author used the Poincare- Lelong formula to show that the density of complex zeros of a system of these…

Mathematical Physics · Physics 2010-11-01 Brian Macdonald

We compute correlation functions of inverse powers and ratios of characteristic polynomials for random matrix models with complex eigenvalues. Compact expressions are given in terms of orthogonal polynomials in the complex plane as well as…

Mathematical Physics · Physics 2011-07-19 G. Akemann , A. Pottier

We study the landscape complexity of the Hamiltonian $X_N(x) +\frac\mu2 \|x\|^2,$ where $X_{N}$ is a smooth Gaussian process with isotropic increments on $\mathbb R^{N}$. This model describes a single particle on a random potential in…

Probability · Mathematics 2023-07-26 Antonio Auffinger , Qiang Zeng

We study the 'critical moments' of subcritical Gaussian multiplicative chaos (GMCs) in dimensions $d \leq 2$. In particular, we establish a fully explicit formula for the leading order asymptotics, which is closely related to large…

Probability · Mathematics 2023-08-02 Jonathan P. Keating , Mo Dick Wong

We study the uniform electron gas with a gap model in the context of density functional theory. Based on this analysis, we construct two local gap models that realize generalized gradient approximation (GGA) correlation functionals…

Other Condensed Matter · Physics 2015-01-22 E. Fabiano , P. E. Trevisanutto , A. Terentjevs , L. A. Constantin

We present an analytical computation of the asymptotic temporal behavior of the information geometric complexity (IGC) of finite-dimensional Gaussian statistical manifolds in the presence of microcorrelations (correlations between…

Mathematical Physics · Physics 2015-05-19 S. A. Ali , C. Cafaro , D. -H. Kim , S. Mancini

The complex Gaussian Multiplicative Chaos (or complex GMC) is informally defined as a random measure $e^{\gamma X} \mathrm{d} x$ where $X$ is a log correlated Gaussian field on $\mathbb R^d$ and $\gamma=\alpha+i\beta$ is a complex…

Probability · Mathematics 2024-05-29 Hubert Lacoin

We formulate a reduced-order strategy for efficiently forecasting complex high-dimensional dynamical systems entirely based on data streams. The first step of our method involves reconstructing the dynamics in a reduced-order subspace of…

Data Analysis, Statistics and Probability · Physics 2017-03-08 Zhong Yi Wan , Themistoklis P. Sapsis

Generalized linear mixed models (GLMMs) are a widely used tool in statistical analysis. The main bottleneck of many computational approaches lies in the inversion of the high dimensional precision matrices associated with the random…

Computation · Statistics 2025-10-08 Andrea Pandolfi , Omiros Papaspiliopoulos , Giacomo Zanella

Gaussian random processes which variances reach theirs maximum values at unique points are considered. Exact asymptotic behaviors of probabilities of large absolute maximums of theirs trajectories have been evaluated using Double Sum Method…

Probability · Mathematics 2019-01-29 E. Hashorva , S. Kobelkov , V. I. Piterbarg

The paper studies the complex 1-dimensional polynomial vector fields with real coefficients under topological orbital equivalence preserving the separatrices of the pole at infinity. The number of generic strata is determined, and a…

Dynamical Systems · Mathematics 2024-07-04 Jonathan Godin , Christiane Rousseau

We study the relation between a complex projective set C in CP^n and the set R in RP^(2n+1) defined by viewing each equation of C as a pair of real equations. Once C is presented by quadratic equations, we can apply a spectral sequence to…

Algebraic Geometry · Mathematics 2011-06-10 Antonio Lerario

We study sampling properties of the zero set of the Gaussian entire function on Fock spaces. Firstly, we relax Seip and Wallst\'en's density and separation conditions for sampling sets on Fock spaces to obtain weighted inequalities for sets…

Probability · Mathematics 2025-08-29 Jeremiah Buckley , Felipe Marceca , Joaquín Singer

In this work, we investigate Gaussian process regression used to recover a function based on noisy observations. We derive upper and lower error bounds for Gaussian process regression with possibly misspecified correlation functions. The…

Statistics Theory · Mathematics 2022-07-20 Wenjia Wang , Bing-Yi Jing

D. Khavinson and G. Swiatek proved that harmonic polynomials p(z)+q(z), where p is holomorphic, q is antiholomorphic, and deg p = n > 1 = deg q, can have at most 3n-2 complex zeros. We show that this bound is sharp for all n by proving a…

Complex Variables · Mathematics 2014-04-04 Lukas Geyer

We start with a rather detailed, general discussion of recent results of the replica approach to statistical mechanics of a single classical particle placed in a random $N (\gg 1)$-dimensional Gaussian landscape and confined by a…

Disordered Systems and Neural Networks · Physics 2008-01-03 Yan V Fyodorov , Ian Williams

Gaussian Process (GP) models provide a flexible framework for prediction and uncertainty quantification. For most covariance functions, however, exact GP prediction with $n$ points scales as $\mathcal{O}(n^3)$, making it prohibitively…

Computation · Statistics 2026-05-29 Samanyu Arora , Christopher J. Geoga