Iterated Random Functions

Iterated random functions are used to draw pictures or simulate large Ising models, among other applications. They offer a method for studying the steady state distribution of a Markov chain, and give useful bounds on rates of convergence in a variety of examples. The present paper surveys the field and presents some new examples. There is a simple unifying idea: the iterates of random Lipschitz functions converge if the functions are contracting on the average.

[1]  A. Wintner,et al.  Distribution functions and the Riemann zeta function , 1935 .

[2]  P. Erdös On the Smoothness Properties of a Family of Bernoulli Convolutions , 1940 .

[3]  Feller William,et al.  An Introduction To Probability Theory And Its Applications , 1950 .

[4]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[5]  F. Spitzer A Combinatorial Lemma and its Application to Probability Theory , 1956 .

[6]  T. W. Anderson On Asymptotic Distributions of Estimates of Parameters of Stochastic Difference Equations , 1959 .

[7]  L. Breiman The Strong Law of Large Numbers for a Class of Markov Chains , 1960 .

[8]  A. Garsia Arithmetic properties of Bernoulli convolutions , 1962 .

[9]  J. Fabius Asymptotic behavior of bayes' estimates , 1963 .

[10]  D. Freedman,et al.  Random distribution functions , 1963 .

[11]  D. Freedman On the Asymptotic Behavior of Bayes' Estimates in the Discrete Case , 1963 .

[12]  J. Hammersley,et al.  Monte Carlo Methods , 1965 .

[13]  D. Freedman,et al.  Measurable sets of measures. , 1964 .

[14]  D. Freedman,et al.  Invariant Probabilities for Certain Markov Processes , 1966 .

[15]  William Feller,et al.  An Introduction to Probability Theory and Its Applications , 1967 .

[16]  P. A. P. Moran,et al.  An introduction to probability theory , 1968 .

[17]  Robert M. Blumenthal,et al.  On continuous collections of measures , 1970 .

[18]  H. Kesten Random difference equations and Renewal theory for products of random matrices , 1973 .

[19]  T. Ferguson A Bayesian Analysis of Some Nonparametric Problems , 1973 .

[20]  D. A. Edwards On the existence of probability measures with given marginals , 1978 .

[21]  W. Vervaat On a stochastic difference equation and a representation of non–negative infinitely divisible random variables , 1979, Advances in Applied Probability.

[22]  J. Sethuraman,et al.  Convergence of Dirichlet Measures and the Interpretation of Their Parameter. , 1981 .

[23]  A. Grincevičius A random difference equation , 1981 .

[24]  H. B. Mitchell Markov Random Fields , 1982 .

[25]  Aleksandr Alekseevich Borovkov,et al.  Asymptotic methods in queuing theory , 1984 .

[26]  Hajime Yamato Characteristic Functions of Means of Distributions Chosen from a Dirichlet Process , 1984 .

[27]  G. Letac A contraction principle for certain Markov chains and its applications , 1986 .

[28]  V. Zolotarev One-dimensional stable distributions , 1986 .

[29]  Y. Kifer Ergodic theory of random transformations , 1986 .

[30]  D. Freedman,et al.  On the consistency of Bayes estimates , 1986 .

[31]  A. Brandt The stochastic equation Yn+1=AnYn+Bn with stationary coefficients , 1986 .

[32]  M. Barnsley,et al.  A new class of markov processes for image encoding , 1988, Advances in Applied Probability.

[33]  M. B. Priestley,et al.  Non-linear and non-stationary time series analysis , 1990 .

[34]  Michael F. Barnsley,et al.  Fractals everywhere , 1988 .

[35]  E. Regazzini,et al.  Distribution Functions of Means of a Dirichlet Process , 1990 .

[36]  J. Elton A multiplicative ergodic theorem for lipschitz maps , 1990 .

[37]  Anthony Quas,et al.  On Representations of Markov Chains by Random Smooth Maps , 1991 .

[38]  G. Letac,et al.  Explicit stationary distributions for compositions of random functions and products of random matrices , 1991 .

[39]  C. Goldie IMPLICIT RENEWAL THEORY AND TAILS OF SOLUTIONS OF RANDOM EQUATIONS , 1991 .

[40]  W. D. Ray Stationary Stochastic Models , 1991 .

[41]  H. Crauel,et al.  Iterated Function Systems and Multiplicative Ergodic Theory , 1992 .

[42]  Hartmut Jürgens,et al.  Introduction to fractals and chaos , 1992 .

[43]  T. Ferguson,et al.  Bayesian nonparametric inference , 1992 .

[44]  P. Bougerol,et al.  Strict Stationarity of Generalized Autoregressive Processes , 1992 .

[45]  P. Franken,et al.  Stationary Stochastic Models. , 1992 .

[46]  F. Baccelli Ergodic Theory of Stochastic Petri Networks , 1992 .

[47]  A. A. Borovkov,et al.  STOCHASTICALLY RECURSIVE SEQUENCES AND THEIR GENERALIZATIONS , 1992 .

[48]  Richard L. Tweedie,et al.  Markov Chains and Stochastic Stability , 1993, Communications and Control Engineering Series.

[49]  S. Kerov Transition probabilities for continual young diagrams and the Markov moment problem , 1993 .

[50]  Geert Jan Olsder,et al.  Synchronization and Linearity: An Algebra for Discrete Event Systems , 1994 .

[51]  S. Rachev,et al.  Limit laws for a stochastic process and random recursion arising in probabilistic modelling , 1995, Advances in Applied Probability.

[52]  B. Solomyak On the random series $\sum \pm \lambda^n$ (an Erdös problem) , 1995 .

[53]  M. Taqqu,et al.  Stable Non-Gaussian Random Processes : Stochastic Models with Infinite Variance , 1995 .

[54]  Y. Peres,et al.  Absolute Continuity of Bernoulli Convolutions, A Simple Proof , 1996 .

[55]  Stephen S. Wilson,et al.  Random iterative models , 1996 .

[56]  竹中 茂夫 G.Samorodnitsky,M.S.Taqqu:Stable non-Gaussian Random Processes--Stochastic Models with Infinite Variance , 1996 .

[57]  David Bruce Wilson,et al.  Exact sampling with coupled Markov chains and applications to statistical mechanics , 1996, Random Struct. Algorithms.

[58]  D. Steinsaltz Zeno's walk: A random walk with refinements , 1997 .

[59]  P. Bougerol,et al.  The random difference equation $X\sb n=A\sb nX\sb {n-1}+B\sb n$ in the critical case , 1997 .

[60]  J. A. Fill An interruptible algorithm for perfect sampling via Markov chains , 1998 .

[61]  P. Diaconis,et al.  Random walks and hyperplane arrangements , 1998 .

[62]  Y. Peres,et al.  Self-similar measures and intersections of Cantor sets , 1998 .

[63]  David Bruce Wilson,et al.  How to Get a Perfectly Random Sample from a Generic Markov Chain and Generate a Random Spanning Tree of a Directed Graph , 1998, J. Algorithms.

[64]  M. Benda A central limit theorem for contractive stochastic dynamical systems , 1998 .

[65]  P. Hanlon,et al.  A combinatorial description of the spectrum for the Tsetlin library and its generalization to hyperplane arrangements , 1999 .

[66]  Olle Häggström,et al.  Characterization results and Markov chain Monte Carlo algorithms including exact simulation for some spatial point processes , 1999 .

[67]  David Steinsaltz,et al.  Locally Contractive Iterated Function Systems , 1999 .

[68]  O. Haggstrom,et al.  On Exact Simulation of Markov Random Fields Using Coupling from the Past , 1999 .

[69]  C. Goldie,et al.  Stability of perpetuities , 2000 .