SIESTA: Extensión del modelo de Markov Oculto (HMM) para el análisis de datos ecológicos y ambientales complejos

El miércoles 9 de setiembre a las 14 horas tendrá luag un Seminario del Istituto de Estadística titulado "Extensión del modelo de Markov Oculto (HMM) para el análisis de datos ecológicos y ambientales complejos". La misma estará a cargo de Vianey Leos Barajas (Assistant Professor-Department of Statistical Science and School of the Environment University of Toronto).

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Resumen

Hidden Markov models (HMMs), and more generally Markov-switching models, are popular frameworks in the analysis of ecological and environmental data collected over time and space as HMMs work on the assumption that what we observe is a result of an underlying latent state. For example, in ecology, animal movement data is collected with the intention to infer behavioral states of the animal (such as foraging or resting), and in environmental sciences, wind turbines collect wind speed data as different
wind speed states relate to different amounts of energy production.

However, much of the popularity associated with HMMs is due to the ease with which its framework can be extended. Here I present two projects that modify the basic HMM structure to model complex ecological and environmental data: including physiological processes in the analysis of animal movement and spatially-coupled HMMs for short-term wind speed forecasting.

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