  The Bernoulli convolution associated to the real $\beta>1$ and the
probability vector $(p_0,..,p_{d-1})$ is a probability measure $\eta_{\beta,p}$
on $\mathbb R$, solution of the self-similarity relation
$\displaystyle\eta=\sum_{k=0}^{d-1}p_k\cdot\eta\circ S_k$ where
$S_k(x)=\frac{x+k}\beta$. If $\beta$ is an integer or a Pisot algebraic number
with finite R\'enyi expansion, $\eta_{\beta,p}$ is sofic and a Markov chain is
naturally associated. If $\beta=b\in\mathbb N$ and $p_0=...=p_{d-1}=\frac1d$,
the study of $\eta_{b,p}$ is close to the study of the order of growth of the
number of representations in base $b$ with digits in $\{0,1,..,d-1\}$. In the
case $b=2$ and $d=3$ it has also something to do with the metric properties of
the continued fractions.
