On-line estimation of hidden Markov model parameters based on the Kullback-Leibler information measure

1993 ◽  
Vol 41 (8) ◽  
pp. 2557-2573 ◽  
Author(s):  
V. Krishnamurthy ◽  
J.B. Moore
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Information Measure ◽  
Model Parameters ◽  
On Line ◽  
Leibler Information

On-line Estimation of Hidden Markov Model Parameters

2000 ◽  
pp. 155-169 ◽  
Author(s):  
Jun Mizuno ◽  
Tatsuya Watanabe ◽  
Kazuya Ueki ◽  
Kazuyuki Amano ◽  
Eiji Takimoto ◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Model Parameters ◽  
On Line

On-Line Monitoring of Pharmaceutical Production Processes Using Hidden Markov Model

2009 ◽  
Vol 98 (4) ◽  
pp. 1487-1498 ◽  
Author(s):  
Hui Zhang ◽  
Zhuangde Jiang ◽  
J.Y. Pi ◽  
H.K. Xu ◽  
R. Du
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Production Processes ◽  
On Line ◽  
Pharmaceutical Production

scholarly journals Detection of contributing object to driving operations based on hidden Markov model

2019 ◽  
Vol 16 (5) ◽  
pp. 172988141987679
Author(s):  
Kohjiro Hashimoto ◽  
Tetsuyasu Yamada ◽  
Takeshi Tsuchiya ◽  
Kae Doki ◽  
Yuki Funabora ◽  
...  
Keyword(s):  
Time Series ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Cognitive Performance ◽  
Traffic Accidents ◽  
Hidden Markov ◽  
Model Parameters ◽  
Detection Accuracy ◽  
Cognitive Support ◽  
Object Based

With increase in the number of elderly people in the Japanese society, traffic accidents caused by elderly driver is considered problematic. The primary factor of the traffic accidents is a reduction in their driving cognitive performance. Therefore, a system that supports the cognitive performance of drivers can greatly contribute in preventing accidents. Recently, the development of devices for visually providing information, such as smart glasses or head up display, is in progress. These devices can provide more effective supporting information for cognitive performance. In this article, we focus on the selection problem of information to be presented for drivers to realize the cognitive support system. It has been reported that the presentation of excessive information to a driver reduces the judgment ability of the driver and makes the information less trustworthy. Thus, indiscriminate presentation of information in the vision of the driver is not an effective cognitive support. Therefore, a mechanism for determining the information to be presented to the driver based on the current driving situation is required. In this study, the object that contributes to execution of avoidance driving operation is regarded as the object that drivers must recognize and present for drivers. This object is called as contributing object. In this article, we propose a method that selects contributing objects among the appeared objects on the current driving scene. The proposed method expresses the relation between the time series change of an appeared object and avoidance operation of the driver by a mathematical model. This model can predict execution timing of avoidance driving operation and estimate contributing object based on the prediction result of driving operation. This model named as contributing model consisted of multi-hidden Markov models. Hidden Markov model is time series probabilistic model with high readability. This is because that model parameters express the probabilistic distribution and its statistics. Therefore, the characteristics of contributing model are that it enables the designer to understand the basis for the output decision. In this article, we evaluated detection accuracy of contributing object based on the proposed method, and readability of contributing model through several experiments. According to the results of these experiments, high detection accuracy of contributing object was confirmed. Moreover, it was confirmed that the basis of detected contributing object judgment can be understood from contributing model.

scholarly journals Pairwise correlations in layered close-packed structures

2015 ◽  
Vol 71 (4) ◽  
pp. 423-443 ◽  
Author(s):  
P. M. Riechers ◽  
D. P. Varn ◽  
J. P. Crutchfield
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Correlation Functions ◽  
Hidden Markov ◽  
Model Parameters ◽  
Pairwise Correlation ◽  
Pairwise Correlations ◽  
Analytical Expressions

Given a description of the stacking statistics of layered close-packed structures in the form of a hidden Markov model, analytical expressions are developed for the pairwise correlation functions between the layers. These may be calculated analytically as explicit functions of model parameters or the expressions may be used as a fast, accurate and efficient way to obtain numerical values. Several examples are presented, finding agreement with previous work as well as deriving new relations.

Confidence intervals for hidden Markov model parameters

2000 ◽  
Vol 53 (2) ◽  
pp. 317-327 ◽  
Author(s):  
Ingmar Visser ◽  
Maartje E. J. Raijmakers ◽  
Peter C. M. Molenaar
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Confidence Intervals ◽  
Hidden Markov ◽  
Model Parameters
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