This Wiki piece is edited here. The process described are nearest to quantum functions that 1t2 logics accommodate.
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In probability theory, a stochastic process, or sometimes random process, is the counterpart to a deterministic process (or deterministic system). Instead of dealing with only one possible reality of how the process might evolve under time (as is the case, for example, for solutions of an ordinary differential equation), in a stochastic or random process there is some indeterminacy in its future evolution described by probability distributions. This means that even if the initial condition (or starting point) is known, there are many possibilities the process might go to, but some paths may be more probable and others less.
In the simplest possible case (discrete time), a stochastic process amounts to a sequence of random variables known as a time series (for example, see Markov chain). Another basic type of a stochastic process is a random field, whose domain is a region of space, in other words, a random function whose arguments are drawn from a range of continuously changing values. One approach to stochastic processes treats them as functions of one or several deterministic arguments (inputs, in most cases regarded as time) whose values (outputs) are random variables: non-deterministic (single) quantities which have certain probability distributions. Random variables corresponding to various times (or points, in the case of random fields) may be completely different. The main requirement is that these different random quantities all have the same type.[1] Although the random values of a stochastic process at different times may be independent random variables, in most commonly considered situations they exhibit complicated statistical correlations.
Familiar examples of processes modeled as stochastic time series include stock market and exchange rate fluctuations.
In relations between variable shifts with respect to market shares, where static and long life conditions of probability distributions are subjected to Shumpeterian creative disruptions, the Shumpeterian functions drive technical transitions between technical families in technical evolution. Wherein discrete categories of technical competency are subject to random fields, which act to de compose variations, of formerly homogeneous series.
Boundaries between fields are blurred and re converge along incremental time slices, where at least one faceted face of a given transition will drive changes in others, until all the variables of the holistic space are functionally reshaped.
Heterogeneous process prevail against inertia as abreactions against homogeneous conditions whose utility has been perceptually realized. |
