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.
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“The moralist and the revolutionary are constantly undermining one another. Marx exploded a hundred tons of dynamite beneath the moralist position, and we are still living in the echo of that tremendous crash. But already, somewhere or other, the sappers are at work and fresh dynamite is being tamped in place to blow Marx at the moon. Then Marx, or somebody like him, will come back with yet more dynamite, and so the process continues, to an end we cannot foresee.”
—George Orwell (1903–1950)