In probability theory, a **continuous-time Markov process** is a stochastic process { *X*(t) : *t* ≥ 0 } that satisfies the Markov property and takes values from a set called the state space; it is the continuous-time version of a Markov chain. The Markov property states that at any times *s* > *t* > 0, the conditional probability distribution of the process at time *s* given the whole history of the process up to and including time *t*, depends only on the state of the process at time *t*. In effect, the state of the process at time *s* is conditionally independent of the history of the process *before* time *t*, given the state of the process *at* time *t*. In simple terms the process can be thought of as memory-less.

Read more about Continuous-time Markov Process: Mathematical Definitions, Embedded Markov Chain, Applications

### Other articles related to "markov process":

**Continuous-time Markov Process**- Applications - Queueing Theory

... Then the number of jobs in the queue is a continuous time

**Markov process**on the non-negative integers ...

### Famous quotes containing the word process:

“Interior design is a travesty of the architectural *process* and a frightening condemnation of the credulity, helplessness and gullibility of the most formidable consumers—the rich.”

—Stephen Bayley (b. 1951)