Search results
Results from the WOW.Com Content Network
Deterministic vs. probabilistic (stochastic). A deterministic model is one in which every set of variable states is uniquely determined by parameters in the model and by sets of previous states of these variables; therefore, a deterministic model always performs the same way for a given set of initial conditions.
In economics, the Ramsey–Cass–Koopmans model is deterministic. 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 ...
Mechanistic models: model the underlying processes directly, with functions and distributions that are based on theoretical reasoning about ecological processes of interest. [3] Ecological models can be deterministic or stochastic. [3] Deterministic models always evolve in the same way from a given starting point. [4]
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 to deterministic models, which assume that populations change in predictable ways, stochastic models account for the inherent randomness in births, deaths, and migration. The birth-death process, [322] a simple stochastic model, describes how populations fluctuate over time due to random births and deaths. These models are ...
In the above example with children's heights, ε is a stochastic variable; without that stochastic variable, the model would be deterministic. Statistical models are often used even when the data-generating process being modeled is deterministic. For instance, coin tossing is, in principle, a deterministic process; yet it is commonly modeled as ...
A stochastic simulation is a simulation of a system that has variables that can change stochastically (randomly) with individual probabilities. [1] Realizations of these random variables are generated and inserted into a model of the system. Outputs of the model are recorded, and then the process is repeated with a new set of random values.
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 ...