Hidden Markov models (HMMs) are flexible tools for clustering dependent data coming from
unknown populations, allowing nonparametric modelling of the population densities …

We consider the problem of estimation in Hidden Markov models with finite state space and
nonparametric emission distributions. Efficient estimators for the transition matrix are …

Thanks to their dependency structure, non-parametric Hidden Markov Models (HMMs) are
able to handle model-based clustering without specifying group distributions. The aim of this …

Multi-stage network attack (MSA) is a serious threat to data security. The high-dimensionality
of the alert data along with the diverse features, leads to poor detection performance for …

Consider the problem of predicting the next symbol given a sample path of length n, whose
joint distribution belongs to a distribution class that may have long-term memory. The goal is …

We investigate the problem of center estimation in the high dimensional binary sub-
Gaussian Mixture Model with Hidden Markov structure on the labels. We first study the …

Three interrelated heuristic techniques for setting the parameters of hidden Markov models
for implementation in pattern recognition algorithms of stochastic processes recorded in the …

This dissertation presents a detailed investigation of the rho-estimation approach applied to
dependent data. The work it contains establishes non-asymptotic deviation bounds with …

Bayesian approaches to statistical modelling allow practitioners to coherently update prior
beliefs based on observed data, and encode these updated beliefs in the posterior …

Thanks to their dependency structure, non-parametric Hidden Markov Models (HMMs) are
able to handle model-based clustering without specifying group distributions. The aim of this …