stochastic function
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Stochastic optimization — (SO) methods are optimization algorithms which incorporate probabilistic (random) elements, either in the problem data (the objective function, the constraints, etc.), or in the algorithm itself (through random parameter values, random choices,… … Wikipedia
Stochastic approximation — methods are a family of iterative stochastic optimization algorithms that attempt to find zeroes or extrema of functions which cannot be computed directly, but only estimated via noisy observations. The first, and prototypical, algorithms of this … Wikipedia
Stochastic resonance — (also known as SR) is observed when noise added to a system improves the systems performance in some fashion. More technically, SR occurs if the signal to noise ratio of a nonlinear system or device increases for moderate values of noise… … Wikipedia
Stochastic control — is a subfield of control theory which deals with the existence of uncertainty in the data. The designer assumes, in a Bayesian probability driven fashion, that a random noise with known probability distribution affects the state evolution and the … Wikipedia
Stochastic volatility — models are used in the field of quantitative finance to evaluate derivative securities, such as options. The name derives from the models treatment of the underlying security s volatility as a random process, governed by state variables such as… … Wikipedia
Stochastic tunneling — (STUN) is an approach to global optimization based on the Monte Carlo method sampling of the function to be minimized. Idea Monte Carlo method based optimization techniques sample the objective function by randomly hopping from the current… … Wikipedia
Stochastic gradient descent — is a general optimization algorithm, but is typically used to fit the parameters of a machine learning model.In standard (or batch ) gradient descent, the true gradient is used to update the parameters of the model. The true gradient is usually… … Wikipedia
Stochastic programming — is a framework for modeling optimization problems that involve uncertainty. Whereas deterministic optimization problems are formulated with known parameters, real world problems almost invariably include some unknown parameters. When the… … Wikipedia
Stochastic Frontier Analysis — is a method of economic modeling. It has its starting point in the stochastic production frontier models simultaneously introduced by Aigner, Lovell and Schmidt (1977) and Meeusen and Van den Broeck (1977).The production frontier model without… … Wikipedia
Stochastic differential equation — A stochastic differential equation (SDE) is a differential equation in which one or more of the terms is a stochastic process, thus resulting in a solution which is itself a stochastic process. SDE are used to model diverse phenomena such as… … Wikipedia
Stochastic process — A stochastic process, or sometimes random process, is the counterpart to a deterministic process (or deterministic system) in probability theory. Instead of dealing with only one possible reality of how the process might evolve under time (as is… … Wikipedia
