  We introduce an algorithm to generate multivariate series of symbols from a
finite alphabet with a given hierarchical structure of similarities. The target
hierarchical structure of similarities is arbitrary, for instance the one
obtained by some hierarchical clustering procedure as applied to an empirical
matrix of Hamming distances. The algorithm can be interpreted as the finite
alphabet equivalent of the recently introduced hierarchically nested factor
model (M. Tumminello et al. EPL 78 (3) 30006 (2007)). The algorithm is based on
a generating mechanism that is different from the one used in the mutation rate
approach. We apply the proposed methodology for investigating the relationship
between the bootstrap value associated with a node of a phylogeny and the
probability of finding that node in the true phylogeny.
