We consider the time dependent probability distribution of a coarse grained
observable Y whose evolution is governed by a discrete time map. If the map is
mixing, the time dependent one-step transition probabilities converge in the
long time limit to yield an ergodic stochastic matrix. The stationary
distribution of this matrix is identical to the asymptotic distribution of Y
under the exact dynamics. The nth time iterate of the baker map is explicitly
computed and used to compare the time evolution of the occupation probabilities
with those of the approximating Markov chain. The convergence is found to be at
least exponentially fast for all rectangular partitions with Lebesgue measure.
In particular, uniform rectangles form a Markov partition for which we find
exact agreement.Comment: 16 pages, 1 figure, uses elsart.sty, to be published in Physica D
Special Issue on Predictability: Quantifying Uncertainty in Models of Complex
Phenomen