Hidden Markov Model is an important approach applied to activity recognition. In the first-order Hidden Markov Model, there is the hypothesis that the transition probability of state and the output probability of observation are only dependent on the current state of the model, which debases the precision of information extraction comparatively. In second-order Hidden Markov Model, the relevanc...

• We explore the predictive power of hidden Markov models in cryptocurrency returns. The 4-state non-homogeneous model has best forecasting performance. Based on profits and risks, distinguishes bull, bear, calm regimes. identifies predictors with state-dependent, linear non-linear effects. most common are series momentum, VIX US Treasury Yield. In this paper, we consider a variety multi-state ...

We prove what appears to be the first concentration of measure result for hidden Markov processes. Our bound is stated in terms of the contraction coefficients of the underlying Markov process, and strictly generalizes the Markov process concentration results of Marton (1996) and Samson (2000). Somewhat surprisingly, the hidden Markov process is at least as “concentrated” as its underlying Mark...

Due to the effective role of Markov models in customer relationship management (CRM), there is a lack of comprehensive literature review which contains all related literatures. In this paper the focus is on academic databases to find all the articles that had been published in 2011 and earlier. One hundred articles were identified and reviewed to find direct relevance for applying Markov models...

Speaker identification performance is almost perfect in neutral talking environments. However, the performance is deteriorated significantly in shouted talking environments. This work is devoted to proposing, implementing, and evaluating new models called Second-Order Circular Suprasegmental Hidden Markov Models (CSPHMM2s) to alleviate the deteriorated performance in the shouted talking environ...

The contribution of this thesis is the development of tractable computational methods for reducing the complexity of two classes of dynamical systems, finite alphabet Hidden Markov Models and Jump Linear Systems with finite parameter space. The reduction algorithms employ convex optimization and numerical linear algebra tools and do not pose any structural requirements on the systems at hand. I...

Hidden Markov models are widely used in the areas of speech recognition and bioinformatics. Hidden Markov models differ from simple Markov models by including hidden states in addition to observable states. For example in bioinformatics, it is not easy to figure out what lies beneath the sequences by using simple Markov models. Once the Hidden Markov Model structure is determined, there are thr...

In current speech recognition research the use of hidden Markov models becomes more and more successful. However, for those who know little or nothing about (hidden) Markov models, no accessible introduction exists to our knowledge. What is a Markov model? How can Markov models be used as recognizers? Why is a hidden Markov model hidden? In this article an attempt has been made to answer these ...