Ecological systems can often be characterised by changes among a finite set of underlying
states pertaining to individuals, populations, communities or entire ecosystems through time …

In this paper we review statistical methods which combine hidden Markov models (HMMs)
and random effects models in a longitudinal setting, leading to the class of so‐called mixed …

PReMiuM is a recently developed R package for Bayesian clustering using a Dirichlet
process mixture model. This model is an alternative to regression models, non …

This book introduces in a systematic manner a general nonparametric theory of statistics on
manifolds, with emphasis on manifolds of shapes. The theory has important and varied …

The hidden Markov model (HMM) has been a workhorse of single-molecule data analysis
and is now commonly used as a stand-alone tool in time series analysis or in conjunction …

We provide a comprehensive overview of latent Markov (LM) models for the analysis of
longitudinal categorical data. We illustrate the general version of the LM model which …

Although Bayesian nonparametric mixture models for continuous data are well developed,
the literature on related approaches for count data is limited. A common strategy is to use a …

We consider the question of Markov chain Monte Carlo sampling from a general stick-
breaking Dirichlet process mixture model, with concentration parameter α α. This paper …

Time series forecasting has been a prosperous filed of science due to its popularity in real-
world applications, yet being challenge in method developments. In medical applications …

In this paper, I review the main results on the asymptotic properties of the posterior
distribution in nonparametric or high-dimensional models. In particular, I explain how …