  The famous results of Koml\'os, Major and Tusn\'ady (see [15] and [17]) state
that it is possible to approximate almost surely the partial sums of size n of
i.i.d. centered random variables in L p (p > 2) by a Wiener process with an
error term of order o(n 1/p). Very recently, Berkes, Liu and Wu [3] extended
this famous result to partial sums associated with functions of an i.i.d.
sequence, provided a condition on a functional dependence measure in L p is
satisfied. In this paper, we adapt the method of Berkes, Liu and Wu to partial
sums of functions of random iterates. Taking advantage of the Markovian
setting, we shall give new dependent conditions, expressed in terms of a
natural coupling (in L $\infty$ or in L 1), under which the strong
approximation result holds with rate o(n 1/p). As we shall see our conditions
are well adapted to a large variety of models, including left random walks on
GL d (R), contracting iterated random functions, autoregressive Lipschitz
processes, and some ergodic Markov chains. We also provide some examples
showing that our L 1-coupling condition is in some sense optimal.

Probability