Efficient backward decoding of high-order hidden Markov models

Engelbrecht, Herman A.; Preez, Johan A. du

doi:10.1016/j.patcog.2009.06.004

Cited by 17 publications

(8 citation statements)

References 29 publications

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“…In contrast to the broad usage of first-order HMMs in applied sciences [66] – [68] , published applications of higher-order HMMs are relatively rare, but they have been demonstrated to be powerful extensions of first-order HMMs for several applications including speech recognition [69] – [76] , image segmentation [77] – [79] , robotic [80] , handwriting recognition [81] , or DNA and protein sequence analysis [82] – [85] . Extensions of the mathematical theory of first-order HMMs to higher-order HMMs are comprehensively described in [86] – [89] .…”

Section: Introductionmentioning

confidence: 99%

Parsimonious Higher-Order Hidden Markov Models for Improved Array-CGH Analysis with Applications to Arabidopsis thaliana

et al. 2012

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Array-based comparative genomic hybridization (Array-CGH) is an important technology in molecular biology for the detection of DNA copy number polymorphisms between closely related genomes. Hidden Markov Models (HMMs) are popular tools for the analysis of Array-CGH data, but current methods are only based on first-order HMMs having constrained abilities to model spatial dependencies between measurements of closely adjacent chromosomal regions. Here, we develop parsimonious higher-order HMMs enabling the interpolation between a mixture model ignoring spatial dependencies and a higher-order HMM exhaustively modeling spatial dependencies. We apply parsimonious higher-order HMMs to the analysis of Array-CGH data of the accessions C24 and Col-0 of the model plant Arabidopsis thaliana. We compare these models against first-order HMMs and other existing methods using a reference of known deletions and sequence deviations. We find that parsimonious higher-order HMMs clearly improve the identification of these polymorphisms. Moreover, we perform a functional analysis of identified polymorphisms revealing novel details of genomic differences between C24 and Col-0. Additional model evaluations are done on widely considered Array-CGH data of human cell lines indicating that parsimonious HMMs are also well-suited for the analysis of non-plant specific data. All these results indicate that parsimonious higher-order HMMs are useful for Array-CGH analyses. An implementation of parsimonious higher-order HMMs is available as part of the open source Java library Jstacs (www.jstacs.de/index.php/PHHMM).

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Section: Introductionmentioning

confidence: 99%

Parsimonious Higher-Order Hidden Markov Models for Improved Array-CGH Analysis with Applications to Arabidopsis thaliana

et al. 2012

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“…These models achieved interesting results in pattern recognition and knowledge extraction in areas such as : speech recognition [MHK97,PBW98,EdP10], hydrology [LABP12], biology [EAA + 09, ETD + 11] and agronomy [MLB06, LBBS + 06].…”

Section: Hmm2 Propertiesmentioning

confidence: 99%

Time space stochastic modelling of agricultural landscapes for environmental issues

Mari

Lazrak²,

Benoît³

2013

Environmental Modelling & Software

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International audienceSince the initial point of Langran, (1993) saying that Geographic Information Systems (GIS) were poorly equipped to handle temporal data, many researchers have sought to integrate the time dimension into GIS,(Roddick,2001). We present a time space modelling approach -- and a generic software named \arpentage -- capable of clustering a territory based on its pluri-annual land-use organization. By adding the ability to represent, locate and visualize temporal changes in the territory, \arpentage provides tools to build a Time-Dominant GIS. One main Markovian assumption is stated: the land-use succession in a given place depends only on the land-use successions in neighbouring plots. By means of stochastic models such as a hierarchical hidden Markov model and a Markov random field, \arpentage performs an unsupervised clustering of a territory in order to reveal patches characterized by time space regularities in the land-use successions. Two case studies are developed involving two territories carrying environmental issues. Those territories have various sizes and are parameterized using long term surveys and/or remote sensing data. In both cases, \arpentage detects, locates and displays in a GIS the temporal changes. This gives valuable information on the spatial and time dynamics of the land-use organization of those territories

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“…Higher-order HMM generalizes first-order HMM by extending the dependency from one immediate previous state to R states, which is defined by a tuple λ = {A, B} [18], [19], [20]. A is the state transition probability matrix with R + 1 dimensions,…”

Section: Higher-order Hidden Markov Modelmentioning

confidence: 99%

“…. o T }, the state sequence that is most likely to have generated the input sequence o and the likelihood can be calculated using the following algorithm [18], [19], [20], which is denoted as HO-HMM algorithm in this paper. 1) Initialization.…”

Section: Higher-order Hidden Markov Modelmentioning

confidence: 99%

Prediction of channel state for cognitive radio using higher-order hidden Markov model

Chen

Qiu

2010

Proceedings of the IEEE SoutheastCon 2010 (SoutheastCon)

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Abstract-Spectrum sensing detects the availability of the radio frequency spectrum, which is essential and vital to cognitive radio. Traditional techniques for spectrum sensing fail to take the latency between spectrum sensing and data transmission into consideration. However, such latency does exist in hardware implementation. Prediction can be utilized to diminish the negative effect of such latency. In this paper, this latency is illustrated, and an approach for prediction of channel state using higher-order hidden Markov model (HMM) is proposed. The predicted channel states are output together with corresponding likelihood probabilities that are helpful to subsequent decision making. Wi-Fi signals have been recorded using a latest advanced ultra-performance digital phosphor oscilloscope (DPO), which are employed to evaluate the performance of the proposed approach. Experimental results show that the proposed approach for prediction of channel state is effective. The proposed approach for prediction of channel state can be used together with traditional spectrum sensing techniques for spectrum sensing with the latency taken into consideration. And it can also be utilized to provide predictive information to upper-level modules of cognitive radio.

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Efficient backward decoding of high-order hidden Markov models

Cited by 17 publications

References 29 publications

Parsimonious Higher-Order Hidden Markov Models for Improved Array-CGH Analysis with Applications to Arabidopsis thaliana

Parsimonious Higher-Order Hidden Markov Models for Improved Array-CGH Analysis with Applications to Arabidopsis thaliana

Time space stochastic modelling of agricultural landscapes for environmental issues

Prediction of channel state for cognitive radio using higher-order hidden Markov model

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