Ioannis Kourouklides

This page contains resources about Hidden Markov Models and Markov Models in general.

Subfields and Concepts[]

  • Markov Models
    • Discrete-time Markov Chain (Discrete-time and Discrete State Space)
    • Discrete-time Harris Chain (Discrete-time and Continuous State Space)
    • Continuous-time Markov Chain / Continuous-time Markov Process / Markov Jump Process
    • Continuous-time Stochastic Process with the Markov property (e.g. Wiener Process)
    • Hidden Markov Model (HMM)
      • Coupled HMM
      • Factorial HMM
      • Autoregressive HMM / Regime Switching Markov Model
      • Hierarchical HMM
      • Hidden Markov Random Field
    • Markov Decision Process
    • Partially Observable Markov Decision Process
    • Hierarchical Markov Models
  • Inference in HMM

 Online Courses[]

Video Lectures[]

Lectures Notes []

Book and Book Chapters[]

  • Puterman, M. L. (2014). Markov Decision Processes: Discrete Stochastic Dynamic Programming. John Wiley & Sons.
  • Murphy, K. P. (2012). "Chapter 17: Markov and hidden Markov models". Machine Learning: A Probabilistic Perspective. MIT Press.
  • Barber, D. (2012). "Chapter 23: Discrete-State Markov Models". Bayesian Reasoning and Machine Learning. Cambridge University Press.
  • Barber, D. (2012). "Chapter 7.5: Markov Decision Process ". Bayesian Reasoning and Machine Learning. Cambridge University Press.
  • Alpaydin, E. (2010). "Chapter 15: Hidden Markov Models". Introduction to machine learning. MIT Press.
  • Koller, D., & Friedman, N. (2009). "Section Hidden Markov Models". Probabilistic Graphical Models. MIT Press.
  • Bishop, C. M. (2006). "Chapter 13: Sequential Data". Pattern Recognition and Machine Learning. Springer.

Scholarly Articles[]



See also[]

Other Resources[]