Structural hidden Markov models: An application to handwritten numeral recognition
Intelligent Data Analysis
Joint segmentation of wind speed and direction using a hierarchical model
Computational Statistics & Data Analysis
ACM Transactions on Asian Language Information Processing (TALIP)
Multilevel mixture Kalman filter
EURASIP Journal on Applied Signal Processing
A statistical multiresolution approach for face recognition using structural hidden Markov models
EURASIP Journal on Advances in Signal Processing
Learning and Inferring Motion Patterns using Parametric Segmental Switching Linear Dynamic Systems
International Journal of Computer Vision
HSCC'07 Proceedings of the 10th international conference on Hybrid systems: computation and control
Conformation-based hidden Markov models: application to human face identification
IEEE Transactions on Neural Networks
The Gaussian mixture MCMC particle algorithm for dynamic cluster tracking
Automatica (Journal of IFAC)
Development of head detection and tracking systems for visual surveillance
Personal and Ubiquitous Computing
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Hidden Markov models (HMMs) represent a very important tool for analysis of signals and systems. In the past two decades, HMMs have attracted the attention of various research communities, including the ones in statistics, engineering, and mathematics. Their extensive use in signal processing and, in particular, speech processing is well documented. A major weakness of conventional HMMs is their inflexibility in modeling state durations. This weakness can be avoided by adopting a more complicated class of HMMs known as nonstationary HMMs. We analyze nonstationary HMMs whose state transition probabilities are functions of time that indirectly model state durations by a given probability mass function and whose observation spaces are discrete. The objective of our work is to estimate all the unknowns of a nonstationary HMM, which include its parameters and the state sequence. To that end, we construct a Markov chain Monte Carlo (MCMC) sampling scheme, where sampling from all the posterior probability distributions is very easy. The proposed MCMC sampling scheme has been tested in extensive computer simulations on finite discrete-valued observed data, and some of the simulation results are presented