Asymptotics of the quantization errors for some Markov-type measures with complete overlaps
Sanguo Zhu

TL;DR
This paper investigates the asymptotic behavior of quantization errors for certain Markov-type measures supported on fractals generated by graph-directed iterated function systems with overlaps, providing explicit formulas and conditions for quantization dimensions.
Contribution
It derives explicit formulas for the quantization dimension of Markov-type measures on overlapping fractals and establishes conditions for the finiteness of quantization coefficients.
Findings
Quantization dimension is explicitly determined for two graph cases.
Lower quantization coefficient is always positive; upper may be infinite in some cases.
Conditions for finiteness of the upper quantization coefficient are provided.
Abstract
Let be a directed graph with vertices . Let be a family of contractive similitudes. For every , let . For , we define . We assume that for every . Let denote the Mauldin-Williams fractal determined by . Let be a positive probability vector and a row-stochastic matrix which serves as an incidence matrix for . We denote by the Markov-type measure associated with and . Let and . Let be the natural projection from…
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Taxonomy
TopicsRandom Matrices and Applications · Mathematical Analysis and Transform Methods · Topological and Geometric Data Analysis
