Search results
Results from the WOW.Com Content Network
Conversely, in a stochastic model—usually called a "statistical model"—randomness is present, and variable states are not described by unique values, but rather by probability distributions. Deductive, inductive, or floating. A deductive model is a logical structure based on a theory. An inductive model arises from empirical findings and ...
The stochastic equivalent is known as real business-cycle theory. As determinism relates to modeling in the natural sciences, a deterministic model [2] uses existing data to model the future behavior of a system. The deterministic model is useful for systems that do not experience frequent or unexpected behavior - unless that behavior is ...
The system designer assumes, in a Bayesian probability-driven fashion, that random noise with known probability distribution affects the evolution and observation of the state variables. Stochastic control aims to design the time path of the controlled variables that performs the desired control task with minimum cost, somehow defined, despite ...
Central subjects in probability theory include discrete and continuous random variables, probability distributions, and stochastic processes (which provide mathematical abstractions of non-deterministic or uncertain processes or measured quantities that may either be single occurrences or evolve over time in a random fashion). Although it is ...
In mathematics, the theory of stochastic processes is an important contribution to probability theory, [29] and continues to be an active topic of research for both theory and applications. [30] [31] [32] The word stochastic is used to describe other terms and objects in mathematics.
In contrast, some authors have argued that randomization can only improve a deterministic algorithm if the deterministic algorithm was poorly designed in the first place. [21] Fred W. Glover [22] argues that reliance on random elements may prevent the development of more intelligent and better deterministic components. The way in which results ...
As academic interest grew, dramatic increases in the power of computers allowed practical applications, including the automatic evolution of computer programs. [8] Evolutionary algorithms are now used to solve multi-dimensional problems more efficiently than software produced by human designers, and also to optimize the design of systems. [9] [10]
Ecological models can be deterministic or stochastic. [3] Deterministic models always evolve in the same way from a given starting point. [4] They represent the average, expected behavior of a system, but lack random variation. Many system dynamics models are deterministic. Stochastic models allow for the direct modeling of the random ...