March 2019
Intermediate to advanced
642 pages
22h 54m
English
Content preview from Python Machine Learning Cookbook - Second Edition
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How it works...
HMM is a model in which the system being modeled is assumed to be a Markov process with unobserved states. A stochastic process is called Markovian when, having chosen a certain instance of t for observation, the evolution of the process, starting with t, depends only on t and does not depend in any way on the previous instances. Thus, a process is Markovian when, given the moment of observation, only this instance determines the future evolution of the process, while this evolution does not depend on the past. In this recipe, we learned how to use HMMs to generate a time series.
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