Unified Treatment of Hidden Markov Switching Models

Details Unified Treatment of Hidden Markov Switching Models Unified Treatment of Hidden Markov Switching Models

2011-04-11

arxiv.org/abs/1104.1992v1

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Machine Learning

Scientific paper

Many real-world problems encountered in several disciplines deal with the modeling of time-series containing different underlying dynamical regimes, for which probabilistic approaches are very often employed. In this paper we describe several such approaches in the common framework of graphical models. We give a unified overview of models previously introduced in the literature, which is simpler and more comprehensive than previous descriptions and enables us to highlight commonalities and differences among models that were not observed in the past. In addition, we present several new models and inference routines, which are naturally derived within this unified viewpoint.

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