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For a multivariate stationary process, we develop explicit representations for the finite predictor coefficient matrices, the finite prediction error covariance matrices and the partial autocorrelation function (PACF) in terms of the…

Probability · Mathematics 2016-09-05 Akihiko Inoue , Yukio Kasahara , Mohsen Pourahmadi

We define two new classes of stochastic processes, called tempered fractional L\'{e}vy process of the first and second kinds (TFLP and TFLP $I\!I$, respectively). TFLP and TFLP $I\!I$ make up very broad finite-variance, generally…

Probability · Mathematics 2019-10-03 Benjamin Cooper Boniece , Gustavo Didier , Farzad Sabzikar

We define martingales on manifolds with time-dependent connection, extending in this way the theory of stochastic processes on manifolds with time-changing geometry initiated by Arnaudon, Coulibaly and Thalmaier (2008). We show that some,…

Probability · Mathematics 2013-05-03 Hongxin Guo , Robert Philipowski , Anton Thalmaier

Coarse-graining is a standard method of extracting a simple Markov process from a more complicated one by identifying states. Here we extend coarse-graining to open Markov processes. An "open" Markov process is one where probability can…

Mathematical Physics · Physics 2019-10-16 John C. Baez , Kenny Courser

A definition of metastable states applicable to arbitrary finite state Markov processes satisfying detailed balance is discussed. In particular, we identify a crucial condition that distinguishes genuine metastable states from other types…

Statistical Mechanics · Physics 2016-08-31 Francois Leyvraz , Hernan Larralde , David P. Sanders

We prove an averaging principle which asserts convergence of diffusion processes on domains separated by semi-permeable membranes, when diffusion coefficients tend to infinity while the flux through the membranes remains constant. In the…

Functional Analysis · Mathematics 2019-08-08 Adam Bobrowski , Bogdan Kazmierczak , Markus Kunze

We derive sufficient conditions for subgeometric f-ergodicity of strongly Markovian processes. We first propose a criterion based on modulated moment of some delayed return-time to a petite set. We then formulate a criterion for polynomial…

Probability · Mathematics 2007-05-23 G. Fort , G. O. Roberts

We study a class of stationary Markov processes with marginal distributions identifiable by moments such that every conditional moment of degree say $m$ is a polynomial of degree at most $m\;\text{.}\;$ We show that then under some…

Probability · Mathematics 2017-05-19 Paweł J. Szabłowski

Given a c\`adl\`ag process $X$ on a filtered measurable space, we construct a version of its semimartingale characteristics which is measurable with respect to the underlying probability law. More precisely, let $\mathfrak{P}_{sem}$ be the…

Probability · Mathematics 2014-07-08 Ariel Neufeld , Marcel Nutz

We introduce the affine ensemble, a class of determinantal point processes (DPP) in the half-plane C^+ associated with the ax+b (affine) group, depending on an admissible Hardy function {\psi}. We obtain the asymptotic behavior of the…

Probability · Mathematics 2022-10-18 Luis Daniel Abreu , Peter Balazs , Smiljana Jakšić

The affine synthesis operator is shown to map the mixed-norm sequence space $\ell^1(\ell^p)$ surjectively onto $L^p(\Rd), 1 \leq p < \infty$, assuming the Fourier transform of the synthesizer does not vanish at the origin and the…

Classical Analysis and ODEs · Mathematics 2007-05-23 Huy-Qui Bui , Richard S. Laugesen

We study the tail behavior of Markov-modulated generalized Ornstein-Uhlenbeck processes -- that is, solutions to Langevin-type stochastic differential equations driven by a background continuous-time Markov chain. To this end, we consider a…

Probability · Mathematics 2026-01-15 Gerold Alsmeyer , Anita Behme

Assume a L\'evy process $X$ on the time interval $[0,1]$ that is an $L_2$-martingale and let $Y$ be either its stochastic exponential or $X$ itself. We consider Riemann-approximations of certain stochastic integrals driven by $Y$ and relate…

Probability · Mathematics 2012-01-04 Christel Geiss , Stefan Geiss , Eija Laukkarinen

Bounded linear types have proved to be useful for automated resource analysis and control in functional programming languages. In this paper we introduce an affine bounded linear typing discipline on a general notion of resource which can…

Programming Languages · Computer Science 2013-07-10 Dan R. Ghica , Alex Smith

We consider a general honest homogeneous continuous-time Markov process with restarts. The process is forced to restart from a given distribution at time moments generated by an independent Poisson process. The motivation to study such…

Probability · Mathematics 2012-06-26 Konstantin Avrachenkov , Alexei Piunovskiy , Zhang Yi

A unification of characteristic mode decomposition for all method-of-moment formulations of field integral equations describing free-space scattering is derived. The work is based on an algebraic link between impedance and transition…

Classical Physics · Physics 2023-01-04 Mats Gustafsson , Lukas Jelinek , Kurt Schab , Miloslav Capek

In this paper we will first introduce the notion of affine structures on a ringed space and then obtain several properties. Affine structures on a ringed space, arising mainly from complex analytical spaces of algebraic schemes over number…

Algebraic Geometry · Mathematics 2010-07-15 Feng-Wen An

For affine processes on finite-dimensional cones, we give criteria for geometric ergodicity - that is exponentially fast convergence to a unique stationary distribution. Ergodic results include both the existence of exponential moments of…

Probability · Mathematics 2021-01-12 Eberhard Mayerhofer , Robert Stelzer , Johanna Vestweber

Let $X_{\alpha}=\{X_{\alpha}(t),t\in T\}$, $\alpha>0$, be an $\alpha$-permanental process with kernel $u(s,t)$. We show that $X^{1/2}_{\alpha}$ is a subgaussian process with respect to the metric $\sigma (s,t)=…

Probability · Mathematics 2017-11-06 Michael B. Marcus , Jay Rosen

Markov processes serve as a universal model for many real-world random processes. This paper presents a data-driven approach for learning these models through the spectral decomposition of the infinitesimal generator (IG) of the Markov…

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