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MATLAB for Neuroscientists, 2nd Edition by Nicholas G. Hatsopoulos, Adam Seth Dickey, Tanya I. Baker, Marc D. Benayoun, Michael E. Lusignan, Pascal Wallisch

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Chapter 32

Markov Models

Abstract

In this chapter, you will learn about modeling sequential phenomena using Markov processes. Simple Markov models will be introduced to characterize sequences in behavior. Hidden Markov models will be introduced with the HMM functions within the Statistics Toolbox in MATLAB®. Finally, hidden Markov models will be used to extract timing data from electrophysiological data by taking advantage of the sequential pattern in the waveform shape.

Keywords

simple Markov models; hidden Markov models; Markov property; motif; syllable; output; state

32.1 Goal of this Chapter

In this chapter, you will learn about modeling sequential phenomena using Markov processes. Simple Markov models will be introduced to characterize sequences ...

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