[2021] Introducción a las Cadenas de Markov {DH}

¿Qué son las cadenas de Markov, cuándo se usan y cómo funcionan?

5 de marzo de 2018·4 minutos de lectura

(Generado a partir de http://setosa.io/ev/markov-chains/)


Imagine that there were two possible states for weather: sunny or cloudy. You can always directly observe the current weather state, and it is guaranteed to always be one of the two aforementioned states.Now, you decide you want to be able to predict what the weather will be like tomorrow. Intuitively, you assume that there is an inherent transition in this process, in that the current weather has some bearing on what the next day’s weather will be. So, being the dedicated person that you are, you collect weather data over several years, and calculate that the chance of a sunny day occurring after a cloudy day is 0.25. You also note that, by extension, the chance of a cloudy day occurring after a cloudy day must be 0.75, since there are only two possible states.You can now use this distribution to predict weather for days to come, based on what the current weather state is at the time.
Una visualización ejemplar del tiempo.

El modelo

Descripción general de un ejemplo de cadena de Markov con estados como círculos y bordes como transiciones
Ejemplo de matriz de transición con 3 estados posibles
Vector de estado inicial con 4 estados posibles


leer contenido completo aquí, [2021] Introducción a las Cadenas de Markov {DH}

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