Work

Cutoff for a class of auto‐regressive models with vanishing additive noise

Year: 2024

Type: article

Abstract: We analyze the convergence rates for a family of auto‐regressive Markov chains on Euclidean space depending on a parameter , where at each step a randomly chosen coordinate is replaced by a noisy damped weighted average of the others. The interest in the model comes from the connection with a certain Bayesian scheme introduced by de Finetti in the analysis of partially exchangeable data. Our main result shows that, when n gets large (corresponding to a vanishing noise), a cutoff phenomenon occurs.

Source: Scandinavian Journal of Statistics

DOI: 10.1111/sjos.12748

Authors: Balázs Gerencsér, Andrea Ottolini

Institutions: Eötvös Loránd University, HUN-REN Alfréd Rényi Institute of Mathematics, University of Washington

Language: English

Topic: Markov Chains and Monte Carlo Methods

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funders: Simons Foundation, Magyar Tudományos Akadémia, Nemzeti Kutatási Fejlesztési és Innovációs Hivatal, National Research, Development and Innovation Office

Awards: G4434563255, G6075461767, G7447468579