COXIAN PHASE TYPE DISTRIBUTION PDF

We'd like to understand how you use our websites in order to improve them. Register your interest. Coxian phase-type distributions are a special type of Markov model that describes duration until an event occurs in terms of a process consisting of a sequence of latent phases. This paper considers the use of Coxian phase-type distributions for modelling patient duration of stay for the elderly in hospital and investigates the potential for using the resulting distribution as a classifying variable to identify common characteristics between different groups of patients according to their anticipated length of stay in hospital. The identification of common characteristics for patient length of stay groups would offer hospital managers and clinicians possible insights into the overall management and bed allocation of the hospital wards. This is a preview of subscription content, log in to check access.

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We'd like to understand how you use our websites in order to improve them. Register your interest. Coxian phase-type distributions are a special type of Markov model that describes duration until an event occurs in terms of a process consisting of a sequence of latent phases. This paper considers the use of Coxian phase-type distributions for modelling patient duration of stay for the elderly in hospital and investigates the potential for using the resulting distribution as a classifying variable to identify common characteristics between different groups of patients according to their anticipated length of stay in hospital.

The identification of common characteristics for patient length of stay groups would offer hospital managers and clinicians possible insights into the overall management and bed allocation of the hospital wards.

This is a preview of subscription content, log in to check access. Rent this article via DeepDyve. Kinsella and V. Google Scholar. Faddy, Examples of fitting structured phase-type distributions, Applied Stochastic Models and Data Analysis 10 — Fazekas, S.

Imre and M. Telek, Modeling and analysis of broadband cellular networks with multimedia connections, Telecommunications Systems 19 3—4 , — Faddy, On inferring the number of phases in a coxian phase-type distribution, Communications of the Statistician — Stochastic Models 14 1—2 — Faddy and S. McClean, Analysing data on lengths of stay of hospital patients using phase-type distributions, Applied Stochastic Models and Data Analysis Cox, A use of complex probabilities in the theory of stochastic processes, Proceedings of the Camb.

Cox and H. Millard, G. Christodoulou and S. Marshall and S. McClean, Conditional phase-type distributions for modelling patient length of stay in hospital, International Transactions in Operational Research 10 — Marshall, S. McClean, C. Shapcott, I. Hastie and P.

Millard, Developing a bayesian belief network for the management of geriatric hospital care, Health Care Management Science Journal 4 23— Nelder and R. Mead, Simplex method for function minimization, Computer Journal 7 — Millard and S. Shapcott and P. Millard, Modelling patient duration of stay to facilitate resource management of geriatric hospitals, Health Care Management Science Journal 5 — Vasilakis and A.

Download references. Correspondence to Adele H. Reprints and Permissions. Marshall, A. Health Care Manage Sci 7, — Download citation. Issue Date : November Search SpringerLink Search. Abstract Coxian phase-type distributions are a special type of Markov model that describes duration until an event occurs in terms of a process consisting of a sequence of latent phases. Immediate online access to all issues from Subscription will auto renew annually.

McClean Authors Adele H. Marshall View author publications. You can also search for this author in PubMed Google Scholar. View author publications. Rights and permissions Reprints and Permissions.

About this article Cite this article Marshall, A.

IRVING FISHER TEORIA CUANTITATIVA DEL DINERO PDF

Phase-type distribution

A phase-type distribution is a probability distribution constructed by a convolution or mixture of exponential distributions. The sequence in which each of the phases occur may itself be a stochastic process. The distribution can be represented by a random variable describing the time until absorption of a Markov process with one absorbing state. Each of the states of the Markov process represents one of the phases.

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