Wiener process


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Wiener process

[′vē·nər ‚prä·səs]
(mathematics)
A stochastic process with normal density at each stage, arising from the study of Brownian motion, which represents the limit of a sequence of experiments. Also known as Gaussian noise.
References in periodicals archive ?
The generalized Wiener process results in an ending stock price [S.
To simulate stock prices following the generalized Wiener process, suppose we track stock prices at discrete time intervals [S.
In addition to using the generalized Wiener process to simulate stock prices, (13) we also use it to simulate currency exchange rates, where [S.
Like the simulation approach, the PDE approach assumes the underlying security price follows a generalized Wiener process (see Equation (2)).
The result of the Wiener process control by suggested adaptive NMPC is shown in Fig.
genocide; group aspects in the physical interpretation of general relativity theory; the periodic system for understanding group processes and work; and the law of the Wiener process and path groups.
The map in (1) has the form of a Wiener process, and the [w.
In Volume 2, continuous time models are explored by presenting the necessary material from continuous martingales, measure theory and stochastic differential equations as models for various assets, such as the Wiener process, Brownian motion, etc.
Following Brennan and Schwartz[4], this study adopts the Wiener process as a suitable tool, as described by Malliaris and Brock[11].
A Wiener process can be used to describe processes such as the movement of a single molecule under Brownian motion, and can be compared with a random walk where transitions or collisions occur continuously rather than at discrete times.
Optimal Sample Size Allocation for Accelerated Degradation Test Based on Wiener Process.
With plenty of examples and exercises, they cover Markov chains, filtering of discrete Markov chains, conditional expectations, filtering of continuous-space Markov chains, Wiener process and continuous time filtering, stationary sequences and prediction of stationary sequences.