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Godland, Philipp: Markov renewal theory in the analysis of random strings and iterated function systems. 2020
Inhalt
Renewal theory in the analysis of random digital trees
Introduction
What has been done?
Structure
Preliminaries
Tries and trie-like structures
Input models
Parameters
Type of main results
Markov renewal theory and notation
Central Markov-modulated sequence
Distributional identity
Lattice
Null-homology
Asymptotic analysis of depth and imbalance factor
Results for Dn
Results for Deltan
Proofs
Weak law of large numbers for Dn
Lp-law of large numbers for Dn
Asymptotic expansion of the mean of Dn
Central limit theorem for Dn
Weak law of large numbers for Deltan
Lp-law of large numbers for Deltan
Central limit theorem for Deltan
Average-case analysis of further characteristic parameters
Main results
Poisson model
Markov renewal theorems
Proofs of main results
Applications
Expected size of a trie
Expected size of a b-trie
Expected size of a PATRICIA-trie
Expected depth in a PATRICIA-trie
Expected external path length of a trie
Expected external path length of a PATRICIA-trie
Appendix
Properties of X0(n)
Depoissonization for stochastically monotone parameters
Auxiliary convergence result
Convergence rates of iterated function systems of Markov-modulated Lipschitz maps by regenerative methods
Introduction
Iterated function systems
Structure
Preliminaries
Measurability and remarks
Dual chain and distributional identity
Elton's theorem
Markov renewal theory and conditions
Convergence rates in Elton's theorem
Main results
Proof of Theorem 7.1 by cyclic decomposition
Convergence rate under polynomial-type moment conditions
Convergence rate under geometric-type moment conditions
Appendix
Kingman’s subadditive ergodic theorem
Prokhorov metric
Some results from the i.i.d. case
Moment results in Situations (1) and (2)
References
Acronyms
List of symbols