a random process in which the probabilities of states in a series depend only on the properties of the immediately preceding state or the next preceeding state, independent of the path by which the preceding state was reached. It is distinguished from a Markov chain in that the states of a Markov process may be continuous as well as discrete.
Stochastic process where consecutive increments are independent from the past.
Markov processes have the following property: given that its current state is known, the probability of any future event of the process is not altered by additional knowledge concerning its past behavior.
a simple stochastic process in which the distribution of future states depends only on the present state and not on how it arrived in the present state
a method used to defined the changes in the probabilities of occurrence of states through time, where the sum of probabilities is one
a process for which, if the present is given, the future and past are independent of each other
a stochastic process for which everything that we know about its future is summarized by its
a stochastic process where all the values are drawn from a discrete set
a stochastic process with particular characteristics which distinguish it from other stochastic processes
a stochastic system evolving in time where the current state determines the statistical properties of the further evolution of the system
Chaos, Fractals, Physics, Mathematics: A Markov process is a stochastic process in which present events depend on the past only through some finite number of generations.
(see Random Walk, Autocorrelation, Autoregressive process, Damped Persistence) is the Autoregressive process of the first order.
An ordered set of discrete random variables, each of which has at any time a given state or value dependent only to the state of the variable immediately before it. It assumes that only the current value of a stochastic (random) variable is important in predicting future values of the variable.
In probability theory, a Markov process is a stochastic process that has the Markov property.
