title: Hidden Markov Models and Dynamical Systems
author:  Andrew M. Fraser 
description: An introduction to hidden Markov models for the dynamical systems community. The book presents algorithms for using HMMs and explains the derivation of those algorithms. It provides introductory material for undergraduate study in engineering, mathematics, or science that includes work in probability, linear algebra and differential equations.
zlib_text: JOURNAL OF AEROSPACE COMPUTING, INFORMATION, AND COMMUNICATION
  Vol. 8, March 2011
  Supervised vs Unsupervised Learning for Operator State
  Modeling in Unmanned Vehicle Settings
  Yves Boussemart∗ , Mary L. Cummings† , Jonathan Las Fargeas‡ , and Nicholas Roy§
  Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
  DOI: 10.2514/1.46767
  In this paper, we model operator states using hidden Markov models applied to human
  supervisory control behaviors. More speciﬁcally, we model the behavior of an operator of
  multiple heterogeneous unmanned vehicle systems. The hidden Markov model framework
  allows the inference of higher operator states from observable operator interaction with a
  computer interface. For example, a sequence of operator actions can be used to compute
  a probability distribution of possible operator states. Such models are capable of detecting
  deviations from expected operator behavior as learned by the model.The difﬁculty with para-
  metric inference models such as hi