Affiliation:
1. Department of Probability and Statistics, Alfréd Rényi Institute of Mathematics Eötvös Loránd University Budapest Hungary
2. Department of Mathematics University of Washington Seattle Washington USA
Abstract
AbstractWe 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.
Funder
Magyar Tudományos Akadémia
Nemzeti Kutatási Fejlesztési és Innovációs Hivatal
Simons Foundation
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