LIMIT THEOREMS FOR MARKOV PROCESSES GENERATED BY ITERATIONS OF RANDOM MAPS

Abstract

Let p(x, dy) be a transition probability function on $(S, \rho)$, where S is a complete separable metric space. Then a Markov process $X_n$ which has p(x, dy) as its transition probability may be generated by random iterations of the form $X_{n+1} = f(X_n, \varepsilon_{n+1})$, where $\varepsilon_n$ is a sequence of independent and identically distributed random variables (See, e.g., Kifer(1986), Bhattacharya and Waymire(1990)).

Keywords

• Markov process;
• invariant probability;
• weak convergence;
• strong law of large numbers;
• functional central limit theorem