stochastic approximation technique - определение. Что такое stochastic approximation technique
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Что (кто) такое stochastic approximation technique - определение

PROBABILITY MODELLING TOOL
Stochastic modeling; Stochastic modelling
Найдено результатов: 1137
Simultaneous perturbation stochastic approximation         
OPTIMIZATION ALGORITHM
Finite Differences Stochastic Approximation
Simultaneous perturbation stochastic approximation (SPSA) is an algorithmic method for optimizing systems with multiple unknown parameters. It is a type of stochastic approximation algorithm.
Stochastic optimization         
Stochastic search; Stochastic optimisation
Stochastic optimization (SO) methods are optimization methods that generate and use random variables. For stochastic problems, the random variables appear in the formulation of the optimization problem itself, which involves random objective functions or random constraints.
Stochastic modelling (insurance)         
"Stochastic" means being or having a random variable. A stochastic model is a tool for estimating probability distributions of potential outcomes by allowing for random variation in one or more inputs over time.
Stochastic process         
  • Wiener]] or [[Brownian motion]] process on the surface of a sphere. The Wiener process is widely considered the most studied and central stochastic process in probability theory.<ref name="doob1953stochasticP46to47"/><ref name="RogersWilliams2000page1"/><ref name="Steele2012page29"/>
  • red}}).
  • Mathematician [[Joseph Doob]] did early work on the theory of stochastic processes, making fundamental contributions, particularly in the theory of martingales.<ref name="Getoor2009"/><ref name="Snell2005"/> His book ''Stochastic Processes'' is considered highly influential in the field of probability theory.<ref name="Bingham2005"/>
  • [[Norbert Wiener]] gave the first mathematical proof of the existence of the Wiener process. This mathematical object had appeared previously in the work of [[Thorvald Thiele]], [[Louis Bachelier]], and [[Albert Einstein]].<ref name="JarrowProtter2004"/>
  • A single computer-simulated '''sample function''' or '''realization''', among other terms, of a three-dimensional Wiener or Brownian motion process for time 0 ≤ t ≤ 2. The index set of this stochastic process is the non-negative numbers, while its state space is three-dimensional Euclidean space.
MATHEMATICAL OBJECT USUALLY DEFINED AS A COLLECTION OF RANDOM VARIABLES
Random function; Theory of random functions; Stochastic processes; Random process; Stochastic transition function; Heterogeneous process; Stochastic effects; Stochastic Process; Random signal; Random system; Random processes; Stochastic model; Stochastic systems; Homogeneous process; Stochastic models; Kolmogorov extension; Stochastic system; Process (stochastic); Discrete-time stochastic process; Stochastic dynamics; Stochastic deaths; Stochastic processe; Stochastic Processes; Real-valued stochastic process; Version (probability theory)
In probability theory and related fields, a stochastic () or random process is a mathematical object usually defined as a family of random variables. Stochastic processes are widely used as mathematical models of systems and phenomena that appear to vary in a random manner.
Stochastic Models         
  • Wiener]] or [[Brownian motion]] process on the surface of a sphere. The Wiener process is widely considered the most studied and central stochastic process in probability theory.<ref name="doob1953stochasticP46to47"/><ref name="RogersWilliams2000page1"/><ref name="Steele2012page29"/>
  • red}}).
  • Mathematician [[Joseph Doob]] did early work on the theory of stochastic processes, making fundamental contributions, particularly in the theory of martingales.<ref name="Getoor2009"/><ref name="Snell2005"/> His book ''Stochastic Processes'' is considered highly influential in the field of probability theory.<ref name="Bingham2005"/>
  • [[Norbert Wiener]] gave the first mathematical proof of the existence of the Wiener process. This mathematical object had appeared previously in the work of [[Thorvald Thiele]], [[Louis Bachelier]], and [[Albert Einstein]].<ref name="JarrowProtter2004"/>
  • A single computer-simulated '''sample function''' or '''realization''', among other terms, of a three-dimensional Wiener or Brownian motion process for time 0 ≤ t ≤ 2. The index set of this stochastic process is the non-negative numbers, while its state space is three-dimensional Euclidean space.
MATHEMATICAL OBJECT USUALLY DEFINED AS A COLLECTION OF RANDOM VARIABLES
Random function; Theory of random functions; Stochastic processes; Random process; Stochastic transition function; Heterogeneous process; Stochastic effects; Stochastic Process; Random signal; Random system; Random processes; Stochastic model; Stochastic systems; Homogeneous process; Stochastic models; Kolmogorov extension; Stochastic system; Process (stochastic); Discrete-time stochastic process; Stochastic dynamics; Stochastic deaths; Stochastic processe; Stochastic Processes; Real-valued stochastic process; Version (probability theory)
Stochastic Models is a peer-reviewed scientific journal that publishes papers on stochastic models. It is published by Taylor & Francis.
Stochastic quantum mechanics         
Stochastic mechanics; Stochastic interpretation
Stochastic quantum mechanics (or the stochastic interpretation) is an interpretation of quantum mechanics.
Stochastic frontier analysis         
Stochastic production frontier; Stochastic Frontier Analysis
Stochastic frontier analysis (SFA) 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).
Stochastic block model         
  • An example of the assortative case for the stochastic block model.
Draft:Stochastic block model; Stochastic blockmodeling
The stochastic block model is a generative model for random graphs. This model tends to produce graphs containing communities, subsets of nodes characterized by being connected with one another with particular edge densities.
Stochastic control         
SUBFIELD OF CONTROL THEORY
Certainty equivalence principle; Stochastic singular control; Certainty equivalence (control theory); Stochastic filtering; Stochastic control theory; Certainty equivalence property; Certainty equivalence; Stochastic filter
Stochastic control or stochastic optimal control is a sub field of control theory that deals with the existence of uncertainty either in observations or in the noise that drives the evolution of the system. 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.
Musical technique         
GROUP OF TECHNIQUES RELATING TO THE COMPOSING, PRODUCTION OR PERFORMANCE OF MUSIC
Technique (music); General Instrumental technique; Performance technique; Instrumental technique; Brass technique; String instrument technique; String technique; Brass instrument technique; Stringed instrument technique; Woodwind technique; Woodwind instrument technique; Percussion technique; Percussion instrument technique; Percussion instrumental technique; Woodwind instrumental technique; Brass instrumental technique; String instrumental technique; Stringed instrumental technique
Musical technique is the ability of instrumental and vocal musicians to exert optimal control of their instruments or vocal cords in order to produce the precise musical effects they desire. Improving one's technique generally entails practicing exercises that improve one's muscular sensitivity and agility.

Википедия

Stochastic modelling (insurance)
This page is concerned with the stochastic modelling as applied to the insurance industry. For other stochastic modelling applications, please see Monte Carlo method and Stochastic asset models. For mathematical definition, please see Stochastic process.

"Stochastic" means being or having a random variable. A stochastic model is a tool for estimating probability distributions of potential outcomes by allowing for random variation in one or more inputs over time. The random variation is usually based on fluctuations observed in historical data for a selected period using standard time-series techniques. Distributions of potential outcomes are derived from a large number of simulations (stochastic projections) which reflect the random variation in the input(s).

Its application initially started in physics. It is now being applied in engineering, life sciences, social sciences, and finance. See also Economic capital.