Abstract

Abstract Asymptotic properties of the sequences (a) { P j } j = 1 ∞ $\{P^{j}\}_{j=1}^{\infty}$ and (b) { j − 1 ∑ i = 0 j − 1 P i } j = 1 ∞ $\{ j^{-1} \sum _{i=0}^{j-1} P^{i}\}_{j=1}^{\infty}$ are studied for g ∈ G = {f ∈ L 1(I) : f ≥ 0 and ‖f ‖ = 1}, where P : L 1(I) → L 1(I) is a Markov operator defined by P f := ∫ P y f d p ( y ) $Pf:= \int P_{y}f\, dp(y) $ for f ∈ L 1; {Py } y∈Y is the family of the Frobenius-Perron operators associated with a family {φy } y∈Y of nonsingular Markov maps defined on a subset I ⊆ ℝ d ; and the index y runs over a probability space (Y, Σ(Y), p). Asymptotic properties of the sequences (a) and (b), of the Markov operator P, are closely connected with the asymptotic properties of the sequence of random vectors x j = φ ξ j ( x j − 1 ) $x_{j}=\varphi_{\xi_{j}}(x_{j-1})$ for j = 1,2, . . .,where { ξ j } j = 1 ∞ $\{\xi_{j}\}_{j=1}^{\infty}$ is a sequence of Y-valued independent random elements with common probability distribution p. An operator-theoretic analogue of Rényi’s Condition is introduced for the family {Py } y∈Y of the Frobenius-Perron operators. It is proved that under some additional assumptions this condition implies the L 1- convergence of the sequences (a) and (b) to a unique g 0 ∈ G. The general result is applied to some families {φy } y∈Y of smooth Markov maps in ℝ d .

Highlights

  • Let a semi-dynamical system evolve according to the rule xj := φ(xj−1), (1.1)

  • In [12, 27] the authors began to study the more general situation when one applies in turn different transformations chosen at random from some family of transformations

  • This work is licensed under the Creative Commons Attribution

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Summary

Introduction

Where φ is a point transformation from a subset I ⊆ Rd (bounded or not), d ≥ 1, into itself. An application of the second kind of Distortion Inequality to smooth Markov maps implies the convergence of the sequence (a), or (b), respectively to a unique g0 ∈ G in the norm of L1, and in the norm of the uniform convergence on each Ik (see Ths. 4.4, 4.8, and 4.5, respectively). Assuming smoothness of the transformations considered, one gets smoothness of their invariant densities

Basic definitions and notations
Convergence theorems
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