Recursive Parameter Estimation and Its Convergence for Multivariate Normal Hidden Markov Inhomogeneous Models


  • Miftahul Fikri ᵃHigh Voltage and High Current Institute, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor, Malaysia; ᵇFaculty of Electricity and Renewable Energy, Institut Teknologi PLN,11750 Jakarta Barat, Jakarta, Indonesia;
  • Zulkurnain Abdul-Malek High Voltage and High Current Institute, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor, Malaysia
  • Mona Riza Mohd Esa High Voltage and High Current Institute, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor, Malaysia
  • Eko Supriyanto School of Biomedical Engineering and Health Sciences, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor Malaysia



Hidden Markov model inhomogeneous, Multivariat normal, Likelihood function, Expectation Maximization, Monotone convergence.


In this paper, will discussed parameter estimation and convergence analysis of multivariate normal hidden inhomogeneous Markov models. The results of this research show that by using the expectation maximization algorithm, a sequence of parameter estimators converges to a stationary point of the likelihood function in a monotonically increasing manner.


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