Markov process


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Mar·kov pro·cess

(mar'kof),
a stochastic process such that the conditional probability distribution for the state at any future instant, given the present state, is unaffected by any additional knowledge of the past history of the system.

Markov process

EBM
A technique of decision analysis that contrasts to standard decision tree analysis, in which a patient moves in one direction through states—e.g., from not treated, to treated, to final outcome—which may include death. In a Markov process, a hypothetical patient could move between states, backwards and forward between continuous ambulatory peritoneal dialysis and haemodialysis, with the caveat that some states—the so-called absorbing states—once entered, cannot be left (e.g., death).

Theoretical medicine
A stochastic process in which the conditional probability distribution for a system’s state at any given instant is unaffected by the system’s previous state.

Markov process

Theoretical medicine A stochastic process in which the conditional probability distribution for a system's state at any given instant is unaffected by that system's previous state

Markov,

(Markoff), Andrei, Russian mathematician, 1865-1922.
Markov chain - number of steps or events in sequence.
Markov chaining - a theory used in psychiatry.
Markov process - a process such that the conditional probability distribution for the state at any future instant, given the present state, is unaffected by any additional knowledge of the past history of the system.
References in periodicals archive ?
The matrix [??] is the transition matrix for the Markov process ([s.sub.t-1], [s.sub.t]).
In the case of the Markov process, a decrease in the correlation time slightly increases the [xi]-coordinate values of the peak position for all the Prandtl numbers.
According to our definition for the Markov process, there is at most one transmission event in each interval.
And there are three types of risk-neutral probability because the stock price follows a first-order Markov process.
Here we suppose that the supply networks model has two switched topology modes, which is dependent on a continuous time Markov process. The two switched topologies are given as Figure 1.
Considering now the Markov process [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] we have E = {1, ..., P, d} where 1, ..., P represent the possible PAs (i.e.
Since [g.sub.t] and [[Rho].sub.t] are assumed to follow a stationary Markov process, it is clear from eq.
These models are "discrete-time" markov processes because probabilities are associated with branches of a tree as discrete units.
Pierre-Andre Chiappori and Roger Guesnerie derive conditions for the existence of equilibria that depend nontrivially on sunspots that follow a k-state Markov process; most previous analysis has dealt only with the case k=2.
The system evolves as a continuous-time Markov process X(t) = {([X.sub.1](t),[X.sub.2](t)), t [greater than or equal to] 0}.
[9] introduce a self-calibrating model for short-term interest rate by assuming that the short rate is governed by a finite state space Markov process. Elliott et al.