The discrete case, generally known as a Markov chain, is discussed on this page. The Markov approach can be applied to the random behaviour of systems that vary discretely or continuously with respect to time and space. This discrete or continuous random variable is known as a stochastic process. Not all stochastic processes can be modelled using the basic Markov approach although there are techniques available for modelling some additional stochastic processes using extensions of this basic method. In order for the basic Markov approach to be applicable, the behaviour of the system must be characterized by a lack of memory, that is, the future states of a system are independent of all past states' except the immediately preceding one.