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This paper presents a Bayesian generative model for dependent Cox point processes, alongside an efficient inference scheme which scales as if the point processes were modelled independently. We can handle missing data naturally, infer…

Machine Learning · Statistics 2014-07-28 Tom Gunter , Chris Lloyd , Michael A. Osborne , Stephen J. Roberts

Let $X$ be a standard Markov process. We prove that a space inversion property of $X$ implies the existence of a Kelvin transform of $X$-harmonic, excessive and operator-harmonic functions and that the inversion property is inherited by…

Probability · Mathematics 2018-08-07 Larbi Alili , Loïc Chaumont , Piotr Graczyk , Tomasz Żak

We consider a class of stochastic dynamical systems, called piecewise deterministic Markov processes, with states $(x, \s)\in \O\times \G$, $\O$ being a region in $\bbR^d$ or the $d$--dimensional torus, $\G$ being a finite set. The…

Statistical Mechanics · Physics 2009-02-25 Alessandra Faggionato , Davide Gabrielli , Marco Ribezzi Crivellari

In this article we investigate the hitting time of some given boundaries for Bessel processes. The main motivation comes from mathematical finance when dealing with volatility models, but the results can also be used in optimal control…

Probability · Mathematics 2013-12-03 Madalina Deaconu , Samuel Herrmann

In this paper, we define a generalised fractional Cox-Ingersoll-Ross process as a square of singular stochastic differential equation with respect to fractional Brownian motion with Hurst parameter H in (0,1) and continuous drift function.…

Probability · Mathematics 2022-07-25 Marc Mukendi Mpanda , Safari Mukeru , Mmboniseni Mulaudzi

When a linear model is adjusted to control for additional explanatory variables the sign of a fitted coefficient may reverse. Here these reversals are studied using coefficients of determination. The resulting theory can be used to…

Methodology · Statistics 2015-03-11 Brian Knaeble , Seth Dutter

We demonstrate the effectiveness of an adaptive explicit Euler method for the approximate solution of the Cox-Ingersoll-Ross model. This relies on a class of path-bounded timestepping strategies which work by reducing the stepsize as…

Computational Finance · Quantitative Finance 2022-01-25 Cónall Kelly , Gabriel Lord , Heru Maulana

The study of both sensitivity analysis and differentiability of the stochastic flow of a reflected process in a convex polyhedral domain is challenging because the dynamics are discontinuous at the boundary of the domain and the boundary of…

Probability · Mathematics 2016-02-08 David Lipshutz , Kavita Ramanan

These notes survey some aspects of discrete-time chaotic calculus and its applications, based on the chaos representation property for i.i.d. sequences of random variables. The topics covered include the Clark formula and predictable…

Probability · Mathematics 2018-06-04 Nicolas Privault

An $N$-dimensional nonlinear Fokker-Planck equation is investigated here by considering the time dependence of the coefficients, where drift-controlled and source terms are present. We exhibit the exact solution based on the generalized…

Statistical Mechanics · Physics 2009-11-07 L. C. Malacarne , R. S. Mendes , I. T. Pedron , E. K. Lenzi

Causal reversibility blends reversibility and causality for concurrent systems. It indicates that an action can be undone provided that all of its consequences have been undone already, thus making it possible to bring the system back to a…

Logic in Computer Science · Computer Science 2024-02-14 Marco Bernardo , Claudio A. Mezzina

The log-Gaussian Cox process is a flexible and popular class of point pattern models for capturing spatial and space-time dependence for point patterns. Model fitting requires approximation of stochastic integrals which is implemented…

Computation · Statistics 2018-10-24 Shinichiro Shirota , Sudipto Banerjee

This article addresses the problem of functional supervised classification of Cox process trajectories, whose random intensity is driven by some exogenous random covariable. The classification task is achieved through a regularized convex…

Statistics Theory · Mathematics 2014-10-16 Gérard Biau , Benoît Cadre , Quentin Paris

Spatial cusps in initial wavefunctions can lead to non-analytic behavior in time. We suggest a method for calculating the short-time behavior in such situations. For these cases, the density does not match its Taylor-expansion in time, but…

Quantum Physics · Physics 2013-05-30 Zeng-hui Yang , Neepa T. Maitra , Kieron Burke

We construct a stable right inverse for the divergence operator in non-cylindrical domains in space-time. The domains are assumed to be H\"older regular in space and evolve continuously in time. The inverse operator is of Bogovskij type,…

Analysis of PDEs · Mathematics 2024-02-28 Olli Saari , Sebastian Schwarzacher

This article focuses on the space-time isogeometric method for a linear time dependent fourth order problem. Using an auxiliary variable, first the problem is split into a system of two second order differential equations and then the…

Numerical Analysis · Mathematics 2025-01-13 Shreya Chauhan , Sudhakar Chaudhary

This paper explicitly computes the transition densities of a spectrally negative stable process with index greater than one, reflected at its infimum. First we derive the forward equation using the theory of sun-dual semigroups. The…

Probability · Mathematics 2016-11-28 Boris Baeumer , Mihály Kovács , Mark M. Meerschaert , René L. Schilling , Peter Straka

Recent advancements in ultrashort and intense X-ray sources have enabled the utilisation of resonant inelastic X-ray scattering (RIXS) as a probing technique for monitoring photoinduced dynamics in molecular systems. To account for dynamic…

Chemical Physics · Physics 2024-03-19 Antonia Freibert , David Mendive-Tapia , Oriol Vendrell , Nils Huse

Understanding directed temporal interactions in multivariate time series is essential for interpreting complex dynamical systems and the predictive models trained on them. We present Causal-INSIGHT, a model-agnostic, post-hoc interpretation…

Machine Learning · Computer Science 2026-03-27 Benjamin Redden , Hui Wang , Shuyan Li

In this paper we define the fractional Cox-Ingersoll-Ross process as $X_t:=Y_t^2\mathbf{1}_{\{t<\inf\{s>0:Y_s=0\}\}}$, where the process $Y=\{Y_t,t\ge0\}$ satisfies the SDE of the form…

Probability · Mathematics 2018-04-06 Yuliya Mishura , Anton Yurchenko-Tytarenko