Chapter 6. Hidden Markov models

This chapter covers

  • Defining interpretive models
  • Using Markov chains to model data
  • Inferring hidden state using a hidden Markov model

If a rocket blows up, someone’s probably going to get fired, so rocket scientists and engineers must be able to make confident decisions about all components and configurations. They do so by physical simulations and mathematical deduction from first principles. You, too, have solved science problems with pure logical thinking. Consider Boyle’s law: pressure and volume of a gas are inversely related under a fixed temperature. You can make insightful inferences from these simple laws that have been discovered about the world. Recently, machine learning has started to play the role of an important sidekick to deductive reasoning.

Rocket science and machine learning aren’t phrases that usually appear together. But nowadays, modeling real-world sensor readings by using intelligent data-driven algorithms is more approachable in the aerospace industry. Also, the use of machine-learning techniques is flourishing in the healthcare and automotive industries. But why?

This influx can be partly attributed to better understanding of interpretable models, which are machine-learning models in which the learned parameters have clear interpretations. If a rocket blows up, for example, an interpretable model might help trace the root cause.

6.1. Example of a not-so-interpretable model

6.2. Markov model

6.3. Hidden Markov model

6.4. Forward algorithm

6.5. Viterbi decoding

6.6. Uses of hidden Markov models

6.7. Application of hidden Markov models

6.8. Summary