Noncolliding processes, matrix-valued processes and determinantal processes
Makoto Katori, Hideki Tanemura

TL;DR
This paper explores noncolliding diffusion processes, their connection to random matrix eigenvalues, and their determinantal structure, including asymptotic behavior as the number of particles grows large.
Contribution
It introduces generalized noncolliding diffusion processes, including inhomogeneous cases, and analyzes their determinantal properties and asymptotic behaviors in relation to random matrix theory.
Findings
Noncolliding processes are determinantal point processes.
The multi-time correlation functions are represented by determinants.
Asymptotic analysis links to eigenvalue distributions in large matrices.
Abstract
A noncolliding diffusion process is a conditional process of independent one-dimensional diffusion processes such that the particles never collide with each other. This process realizes an interacting particle system with long-ranged strong repulsive forces acting between any pair of particles. When the individual diffusion process is a one-dimensional Brownian motion, the noncolliding process is equivalent in distribution with the eigenvalue process of an Hermitian-matrix-valued process, which we call Dyson's model. For any deterministic initial configuration of particles, distribution of particle positions of the noncolliding Brownian motion on the real line at any fixed time is a determinantal point process. We can prove that the process is determinantal in the sense that the multi-time correlation function for any chosen series of times, which determines…
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Taxonomy
TopicsRandom Matrices and Applications · Stochastic processes and statistical mechanics · Advanced Combinatorial Mathematics
