Hierarchical modeling is a fundamental concept in Bayesian statistics. The basic idea is that
parameters are endowed with distributions which may themselves introduce new …

Subword units are an effective way to alleviate the open vocabulary problems in neural
machine translation (NMT). While sentences are usually converted into unique subword …

This volume hopes to fill a growing need for the description of current methodology in public
health surveillance. Recent advances in syndromic surveillance and, more generally, in …

Hidden Markov models—most often abbreviated to the acronym “HMMs”—are one of the
most successful statistical modelling ideas that have came up in the last forty years: the use …

Reveals How HMMs Can Be Used as General-Purpose Time Series ModelsImplements all
methods in RHidden Markov Models for Time Series: An Introduction Using R applies …

Modelling based on finite mixture distributions is a rapidly developing area with the range of
applications exploding. Finite mixture models are nowadays applied in such diverse areas …

Drawing on the authors' extensive research in the analysis of categorical longitudinal data,
this book focuses on the formulation of latent Markov models and the practical use of these …

We demonstrate how to perform direct simulation from the posterior distribution of a class of
multiple changepoint models where the number of changepoints is unknown. The class of …

We consider the problem of speaker diarization, the problem of segmenting an audio
recording of a meeting into temporal segments corresponding to individual speakers. The …

This research models the dynamics of customer relationships using typical transaction data.
Our proposed model permits not only capturing the dynamics of customer relationships, but …