Analysis of a risk-sensitive control problem for hidden Markov chains

1999 ◽  
Vol 44 (5) ◽  
pp. 1093-1100 ◽  
Author(s):  
D. Hernandez-Hernandez ◽  
S.I. Marcus ◽  
P.J. Fard
Keyword(s):  
Markov Chains ◽  
Control Problem ◽  
Hidden Markov ◽  
Hidden Markov Chains ◽  
Risk Sensitive ◽  
Risk Sensitive Control

On White Noises Driven by Hidden Markov Chains

1997 ◽  
Vol 18 (6) ◽  
pp. 553-578 ◽  
Author(s):  
Christian Francq ◽  
Michel Roussignol
Keyword(s):  
Markov Chains ◽  
Hidden Markov ◽  
Hidden Markov Chains ◽  
White Noises

An adaptive FEC algorithm using hidden Markov chains

2003 ◽  
Vol 31 (2) ◽  
pp. 11-13 ◽  
Author(s):  
Flávio P. Duarte ◽  
Edmundo de Souza e Silva ◽  
Don Towsley
Keyword(s):  
Markov Chains ◽  
Hidden Markov ◽  
Hidden Markov Chains ◽  
Adaptive Fec

Delirium superimposed on dementia: defining disease states and course from longitudinal measurements of a multivariate index using latent class analysis and hidden Markov chains

2011 ◽  
Vol 23 (10) ◽  
pp. 1659-1670 ◽  
Author(s):  
Antonio Ciampi ◽  
Alina Dyachenko ◽  
Martin Cole ◽  
Jane McCusker
Keyword(s):  
Mental Disorders ◽  
Markov Chains ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Hidden Markov ◽  
The Elderly ◽  
Population Heterogeneity ◽  
Class Analysis ◽  
Hidden Markov Chains ◽  
Time Point

ABSTRACTBackground: The study of mental disorders in the elderly presents substantial challenges due to population heterogeneity, coexistence of different mental disorders, and diagnostic uncertainty. While reliable tools have been developed to collect relevant data, new approaches to study design and analysis are needed. We focus on a new analytic approach.Methods: Our framework is based on latent class analysis and hidden Markov chains. From repeated measurements of a multivariate disease index, we extract the notion of underlying state of a patient at a time point. The course of the disorder is then a sequence of transitions among states. States and transitions are not observable; however, the probability of being in a state at a time point, and the transition probabilities from one state to another over time can be estimated.Results: Data from 444 patients with and without diagnosis of delirium and dementia were available from a previous study. The Delirium Index was measured at diagnosis, and at 2 and 6 months from diagnosis. Four latent classes were identified: fairly healthy, moderately ill, clearly sick, and very sick. Dementia and delirium could not be separated on the basis of these data alone. Indeed, as the probability of delirium increased, so did the probability of decline of mental functions. Eight most probable courses were identified, including good and poor stable courses, and courses exhibiting various patterns of improvement.Conclusion: Latent class analysis and hidden Markov chains offer a promising tool for studying mental disorders in the elderly. Its use may show its full potential as new data become available.

Identification of hidden Markov chains governing dependent credit-rating migrations

2017 ◽  
Vol 48 (1) ◽  
pp. 75-87 ◽  
Author(s):  
D. V. Boreiko ◽  
S. Y. Kaniovski ◽  
Y. M. Kaniovski ◽  
G. Ch. Pflug
Keyword(s):  
Markov Chains ◽  
Credit Rating ◽  
Hidden Markov ◽  
Hidden Markov Chains
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