Presentation16: Using Maximum Likelihood Estimation to Calibrate a Discrete Time Markov Model

Описание к видео Presentation16: Using Maximum Likelihood Estimation to Calibrate a Discrete Time Markov Model

In this video lesson, we return to our example that is inspired by McAuliffe's paper on vegetation succession in desert plant communities in order to explore a method, based upon maximum likelihood estimation, for estimating the unknown transition probabilities of a discrete time markov model from the transition counts we can observe in a time series of the dominant vegetation categories seen in the landscape over time.

This video lesson supports the Probability and Statistics Core Learning Resource (CLR) (https://mathsciresearchlaunchpad.word...) at the Mathematical Science Research Launchpad.

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