Métodos de inferencia estadística para entrenamiento de modelos ocultos de Markov

Ricardo Antonio Mendoza León

Resumen


Este documento presenta una revisión general de las diferentes aproximaciones y métodos en inferencia estadística, aplicados al problema de entrenamiento o ajuste de parámetros en Modelos Ocultos de Markov. Se tratarán los algoritmos EM (Expectation Maximization) y GEM (Generalized Expectation Maximization), el marco de modelos gráficos y sus algoritmos ML (Maximum Likelihood) y MAP (Maximum a Posteriori), así como modelos de conjunto, variacionales y métodos de muestreo MCMC (Markov Chain Montecarlo).

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Referencias


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DOI: http://dx.doi.org/10.15765/e.v1i1.191

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