scholarly journals Positive Sequential Data Modeling Using Continuous Hidden Markov Models Based on Inverted Dirichlet Mixtures

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 172341-172349 ◽  
Author(s):  
Ru Wang ◽  
Wentao Fan
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Data Modeling ◽  
Sequential Data ◽  
Dirichlet Mixtures ◽  
Inverted Dirichlet

Factorial Hidden Markov Models and the Generalized Backfitting Algorithm

2002 ◽  
Vol 14 (10) ◽  
pp. 2415-2437 ◽  
Author(s):  
Robert A. Jacobs ◽  
Wenxin Jiang ◽  
Martin A. Tanner
Keyword(s):  
Parameter Estimation ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Additive Models ◽  
Series Data ◽  
Approximate Inference ◽  
Sequential Data ◽  
Factorial Hidden Markov Models ◽  
Backfitting Algorithm

Previous researchers developed new learning architectures for sequential data by extending conventional hidden Markov models through the use of distributed state representations. Although exact inference and parameter estimation in these architectures is computationally intractable, Ghahramani and Jordan (1997) showed that approximate inference and parameter estimation in one such architecture, factorial hidden Markov models (FHMMs), is feasible in certain circumstances. However, the learning algorithm proposed by these investigators, based on variational techniques, is difficult to understand and implement and is limited to the study of real-valued data sets. This chapter proposes an alternative method for approximate inference and parameter estimation in FHMMs based on the perspective that FHMMs are a generalization of a well-known class of statistical models known as generalized additive models (GAMs; Hastie & Tibshirani, 1990). Using existing statistical techniques for GAMs as a guide, we have developed the generalized backfitting algorithm. This algorithm computes customized error signals for each hidden Markov chain of an FHMM and then trains each chain one at a time using conventional techniques from the hidden Markov models literature. Relative to previous perspectives on FHMMs, we believe that the viewpoint taken here has a number of advantages. First, it places FHMMs on firm statistical foundations by relating them to a class of models that are well studied in the statistics community, yet it generalizes this class of models in an interesting way. Second, it leads to an understanding of how FHMMs can be applied to many different types of time-series data, including Bernoulli and multinomial data, not just data that are real valued. Finally, it leads to an effective learning procedure for FHMMs that is easier to understand and easier to implement than existing learning procedures. Simulation results suggest that FHMMs trained with the generalized backfitting algorithm are a practical and powerful tool for analyzing sequential data.

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