We develop a new Bayesian modelling framework for the class of higher-order, variable-
memory Markov chains, and introduce an associated collection of methodological tools for …

The Partition Markov Model characterizes the process by a partition L of the state space,
where the elements in each part of L share the same transition probability to an arbitrary …

We present deviation bounds for self-normalized averages and applications to estimation
with a random number of observations. The results rely on a peeling argument in …

The Thermodynamic Formalism provides a rigorous mathematical framework for studying
quantitative and qualitative aspects of dynamical systems. At its core, there is a variational …

Context tree models have been introduced by Rissanen in [25] as a parsimonious
generalization of Markov models. Since then, they have been widely used in applied …

Rhythm analysis of written texts focuses on literary analysis and it mainly considers poetry.
In this paper we investigate the relevance of rhythmic features for categorizing texts in …

In this article we address two related issues on the learning of probabilistic sequences of
events. First, which features make the sequence of events generated by a stochastic chain …

Using a new probabilistic approach we model the relationship between sequences of
auditory stimuli generated by stochastic chains and the electroencephalographic (EEG) data …

We consider a (multidimensional) additive functional of semi-Markov chain, defined by an
ergodic Markov chain with a finite number of states. The distribution of random vectors …

We introduce a new index to detect dependence in trivariate distributions. The index is
based on the maximization of the coefficients of directional dependence over the set of …