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We present a robust method which translates information on the speed of coming down from infinity of a genealogical tree into sampling formulae for the underlying population. We apply these results to population dynamics where the genealogy…

Probability · Mathematics 2012-02-01 Julien Berestycki , Nathanael Berestycki , Vlada Limic

Spatial Poisson point processes on finite-dimensional Euclidean space provide fundamental mathematical tools for modeling random spatial point patterns. In this paper, we introduce and analyze several Poisson-type spatial point processes.…

Probability · Mathematics 2026-01-26 Pradeep Vishwakarma

The Fleming-Viot process with parent-independent mutation process is one particular neutral population genetic model. As time goes by, some initial species are replaced by mutated ones gradually. Once the population mutation rate is high,…

Probability · Mathematics 2016-03-16 Youzhou Zhou

The goal of this paper is to unify the lookdown representation and the stochastic flow of bridges, which are two approaches to construct the $\Lambda$-Fleming-Viot process along with its genealogy. First we introduce the stochastic flow of…

Probability · Mathematics 2014-06-27 Cyril Labbé

We consider a continuous-time Bienaym\'e-Galton-Watson process with logistic competition in a regime of weak competition, or equivalently of a large carrying capacity. Individuals reproduce at random times independently of each other but…

Probability · Mathematics 2025-01-29 Raphaël Forien

Fleming-Viot diffusions are widely used stochastic models for population dynamics which extend the celebrated Wright-Fisher diffusions. They describe the temporal evolution of the relative frequencies of the allelic types in an ideally…

Computation · Statistics 2026-01-07 Filippo Ascolani , Stefano Damato , Matteo Ruggiero

We study the large time behavior of classical solutions to the two-dimensional Vlasov-Poisson (VP) and relativistic Vlasov-Poisson (RVP) systems launched by radially-symmetric initial data with compact support. In particular, we prove that…

Analysis of PDEs · Mathematics 2022-09-20 Jonathan Ben-Artzi , Baptiste Morisse , Stephen Pankavich

Anomalous diffusion and L\'evy flights, which are characterized by the occurrence of random discrete jumps of all scales, have been observed in a plethora of natural and engineered systems, ranging from the motion of molecules to climate…

Dynamical Systems · Mathematics 2023-09-04 Chunxi Jiao , Georg A. Gottwald

We consider an individual-based spatially structured population for Darwinian evolution in an asexual population. The individuals move randomly on a bounded continuous space according to a reflected brownian motion. The dynamics involves…

Probability · Mathematics 2015-09-08 Helene Leman

We derive a nonparametric higher-order asymptotic expansion for small-time changes of conditional characteristic functions of It\^o semimartingale increments. The asymptotics setup is of joint type: both the length of the time interval of…

Statistical Finance · Quantitative Finance 2025-02-12 Carsten H. Chong , Viktor Todorov

We review localization with non-Hermitian time evolution as applied to simple models of population biology with spatially varying growth profiles and convection. Convection leads to a constant imaginary vector potential in the…

Disordered Systems and Neural Networks · Physics 2009-10-31 Karin A. Dahmen , David R. Nelson , Nadav M. Shnerb

Consider compound Poisson processes with negative drift and no negative jumps, which converge to some spectrally positive L\'evy process with non-zero L\'evy measure. In this paper we study the asymptotic behavior of the local time process,…

Probability · Mathematics 2013-05-24 Amaury Lambert , Florian Simatos

The asymptotic behavior of a stochastic network represented by a birth and death processes of particles on a compact state space is analyzed. Births: Particles are created at rate $\lambda_+$ and their location is independent of the current…

Probability · Mathematics 2010-05-12 Philippe Robert

A continuum growth model is introduced. The state at time $t$, $S_t$, is a subset of $\mathbb{R}^d$ and consists of a connected union of randomly sized Euclidean balls, which emerge from outbursts at their center points. An outburst occurs…

Probability · Mathematics 2015-09-24 Maria Deijfen

Applying standard Markov chain Monte Carlo (MCMC) algorithms to large data sets is computationally infeasible. The recently proposed stochastic gradient Langevin dynamics (SGLD) method circumvents this problem in three ways: it generates…

Methodology · Statistics 2015-09-22 Sebastian J. Vollmer , Konstantinos C. Zygalakis , and Yee Whye Teh

We characterize the local smoothness and the asymptotic growth rate of the L\'evy white noise. We do so by characterizing the weighted Besov spaces in which it is located. We extend known results in two ways. First, we obtain new bounds for…

Probability · Mathematics 2020-12-14 Shayan Aziznejad , Julien Fageot

We investigate the behaviour of an establishing mutation which is subject to rapidly fluctuating selection under the Lambda-Fleming-Viot model and show that under a suitable scaling it converges to the Feller diffusion in a random…

Probability · Mathematics 2019-01-15 Jonathan Chetwynd-Diggle , Aleksander Klimek

The paper reviews the results obtained for spatial population models and the evolution of the genealogies of these populations during the last decade by the author and his coworkers. The focus is on their large scale behaviour and on the…

Probability · Mathematics 2019-07-17 Andreas Greven

The Fleming-Viot measure-valued diffusion is a Markov process describing the evolution of (allelic) types under mutation, selection and random reproduction. We enrich this process by genealogical relations of individuals so that the random…

Probability · Mathematics 2012-11-30 Andrej Depperschmidt , Andreas Greven , Peter Pfaffelhuber

The support vector machine (SVM) and minimum Euclidean norm least squares regression are two fundamentally different approaches to fitting linear models, but they have recently been connected in models for very high-dimensional data through…

Machine Learning · Computer Science 2021-10-28 Navid Ardeshir , Clayton Sanford , Daniel Hsu