Variance and Covariance of Several Simultaneous Outputs of a Markov
Chain
Authors: Sara Kropf
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Sara Kropf
The partial sum of the states of a Markov chain or more generally a Markov
source is asymptotically normally distributed under suitable conditions. One of
these conditions is that the variance is unbounded. A simple combinatorial
characterization of Markov sources which satisfy this condition is given in
terms of cycles of the underlying graph of the Markov chain. Also Markov
sources with higher dimensional alphabets are considered.
Furthermore, the case of an unbounded covariance between two coordinates of
the Markov source is combinatorically characterized. If the covariance is
bounded, then the two coordinates are asymptotically independent.
The results are illustrated by several examples, like the number of specific
blocks in $0$-$1$-sequences and the Hamming weight of the width-$w$
non-adjacent form.