Probability of maintaining or attaining a structure in one step

1987 ◽  
Vol 24 (4) ◽  
pp. 1006-1011 ◽  
Author(s):  
G. Abdallaoui
Keyword(s):  
Markov Chain ◽  
Discrete Time ◽  
Transition Probabilities ◽  
Markov Chain Model ◽  
Chain Model ◽  
Discrete Time Markov Chain ◽  
One Step ◽  
Manpower System

Our concern is with a particular problem which arises in connection with a discrete-time Markov chain model for a graded manpower system. In this model, the members of an organisation are classified into distinct classes. As time passes, they move from one class to another, or to the outside world, in a random way governed by fixed transition probabilities. In this paper, the emphasis is placed on evaluating exact values of the probabilities of attaining and maintaining a structure.

Probability of maintaining or attaining a structure in one step

1987 ◽  
Vol 24 (04) ◽  
pp. 1006-1011 ◽  
Author(s):  
G. Abdallaoui
Keyword(s):  
Markov Chain ◽  
Discrete Time ◽  
Transition Probabilities ◽  
Markov Chain Model ◽  
Chain Model ◽  
Discrete Time Markov Chain ◽  
One Step ◽  
Manpower System

Our concern is with a particular problem which arises in connection with a discrete-time Markov chain model for a graded manpower system. In this model, the members of an organisation are classified into distinct classes. As time passes, they move from one class to another, or to the outside world, in a random way governed by fixed transition probabilities. In this paper, the emphasis is placed on evaluating exact values of the probabilities of attaining and maintaining a structure.

scholarly journals Markov Chain Modeling of HIV, Tuberculosis, and Hepatitis B Transmission in Ghana

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Clement Twumasi ◽  
Louis Asiedu ◽  
Ezekiel N. N. Nortey
Keyword(s):  
Markov Chain ◽  
Hepatitis B ◽  
Discrete Time ◽  
Transition Probabilities ◽  
Markov Chain Model ◽  
Chain Model ◽  
Disease Dynamics ◽  
Epidemiological Models ◽  
Discrete Time Markov Chain ◽  
Expected Time

Several mathematical and standard epidemiological models have been proposed in studying infectious disease dynamics. These models help to understand the spread of disease infections. However, most of these models are not able to estimate other relevant disease metrics such as probability of first infection and recovery as well as the expected time to infection and recovery for both susceptible and infected individuals. That is, most of the standard epidemiological models used in estimating transition probabilities (TPs) are not able to generalize the transition estimates of disease outcomes at discrete time steps for future predictions. This paper seeks to address the aforementioned problems through a discrete-time Markov chain model. Secondary datasets from cohort studies were collected on HIV, tuberculosis (TB), and hepatitis B (HB) cases from a regional hospital in Ghana. The Markov chain model revealed that hepatitis B was more infectious over time than tuberculosis and HIV even though the probability of first infection of these diseases was relatively low within the study population. However, individuals infected with HIV had comparatively lower life expectancies than those infected with tuberculosis and hepatitis B. Discrete-time Markov chain technique is recommended as viable for modeling disease dynamics in Ghana.

Manifest and Latent Markov Chain Models for Categorical Panel Data

1988 ◽  
Vol 13 (4) ◽  
pp. 299-312 ◽  
Author(s):  
Rolf Langeheine
Keyword(s):  
Markov Chain ◽  
Discrete Time ◽  
Markov Chain Model ◽  
Order Effects ◽  
Chain Model ◽  
Continuous Time Markov Chain ◽  
Discrete Time Markov Chain ◽  
Markov Chain Models ◽  
Starting Point ◽  
Recent Developments

The starting point of this paper is a 3 × 3 × 3 table of repeated behavior ratings of children, which has been previously analyzed by Plewis (1981) using manifest discrete time and continuous time Markov chain models. Potential reasons for the ubiquitous misfit of the manifest discrete time Markov chain model are outlined. It is proposed, instead, to make use of more recent developments in latent discrete time Markov chain modeling that simultaneously address the main problems of heterogeneity, measurement error, stationarity, and order effects.

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