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On Conditional Markov Processes

Theory of Probability & Its Applications, 1960
In this paper a pair of random processes $X_t $, $Y_t $, which conjunctly form the Markov process $Z_t $ is considered. The conditional distribution of the process $Y_t $ for the condition of a known realization of the process $X_t $ during some time interval is examined. E. B.
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Composable Markov processes [PDF]

Journal of Applied Probability, 1970
Many phenomena studied in the social sciences and elsewhere are complexes of more or less independent characteristics which develop simultaneously. Such phenomena may often be realistically described by time-continuous finite Markov processes. In order to define such a model which will take care of all the relevant a priori information, there ought to ...
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Markov Processes

1983
Probability theory is the tool that must be used to describe the randomness visible in the world around us, but by itself probability theory is not enough. We need to be able to describe the time evolution of the probabilities that describe the mixture of predictability and chance observed.
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Functions of Markov Processes

Zeitschrift f�r Wahrscheinlichkeitstheorie und Verwandte Gebiete, 1966
This chapter is concerned with many-to-one functions of Markov processes. Neither the Markov property nor the Chapman-Kolmogorov equation are generally satisfied by the derived processes determined by such functions. The first section considers special circumstances under which the Chapman-Kolmogorov equation is still satisfied by the derived process ...
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On aggregated Markov processes

Journal of Applied Probability, 1986
A finite-state Markov process is aggregated into several groups. What can be learned about the underlying process from the aggregated one? We provide some partial answers to this question.
D. R. Fredkin, John Rice
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Piecewise Markov Processes

SIAM Journal on Applied Mathematics, 1973
A piecewise Markov process is a discrete-state, continuous-parameter stochastic process which is Markovian within contiguous time-segments. Starting at the beginning of a segment in some initial state, the process evolves in a Markovian manner until the segment terminates at a random time whose distribution is completely determined by the initial state.
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Markov properties of a Markov process [PDF]

Zeitschrift f�r Wahrscheinlichkeitstheorie und Verwandte Gebiete, 1981
Ronald K. Getoor, M. J. Sharpe
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Conditional Markov Processes

Theory of Probability & Its Applications, 1960
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Markov population processes

Journal of Applied Probability, 1969
SummaryThe processes of the title have frequently been used to represent situations involving numbers of individuals in different categories or colonies. In such processes the state at any time is represented by the vectorn= (n1,n2, …,nk),where ntis the number of individuals in theith colony, and the random evolution ofnis supposed to be that of a ...
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Relaxed Markov processes [PDF]

Advances in Applied Probability, 1983
The concept of relaxing a Markov process is introduced; this is the creation of additional transitions between ergodic classes of the process in such a way as to conserve the existing equilibrium distribution within ergodic classes. The ‘open' version of a ‘closed' model of migration, polymerisation etc. often has this character.
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