Stochastic modeling

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Stochastic Modeling

Any of several methods for measuring the probability of distribution of a random variable. That is, a stochastic model measures the likelihood that a variable will equal any of a universe of amounts. It is used in technical analysis to predict market movements. Insurance companies also use stochastic modeling to estimate their assets and liabilities because, due to the nature of the insurance business, these are not known quantities.

Stochastic modeling.

Stochastic modeling is a statistical process that uses probability and random variables to predict a range of probable investment performances.

The mathematical principles behind stochastic modeling are complex, so it's not something you can do on your own.

But based on information you provide about your age, investments, and risk tolerance, financial analysts may use stochastic modeling to help you evaluate the probability that your current investment portfolio will allow you to meet your financial goals.

Appropriately enough, the term stochastic comes from the Greek word meaning "skillful in aiming."

References in periodicals archive ?
Liu H, Zhao WZ, He Z, Zhang L (2007) Stochastic modelling of soil moisture dynamics in a grassland of Qilian Mountain at point scale.
Khouider, 2008: An applied mathematics perspective on stochastic modelling for climate.
66 of Stochastic Modelling and Applied Probability, Springer Berlin Heidelberg, second edition, 2012.
Bellhouse, "Stochastic modelling of interest rates with applications to life contingencies," The Journal of Risk and Insurance, vol.
Computational approaches that have been hugely popular and found important applications include computational modeling, Bayesian and graphical models, machine learning, deep-learning, pattern recognition, optimization, spectral and pseudospectral analysis, stochastic modelling, iterative system model adaptation, and multiscale multiphysics analysis to name a few.
Parthasarathy, "On stochastic modelling of linear circuits," International Journal of Circuit Theory and Applications, vol.
of Bucharest) update considerably as well as translate their 1984 Elements of Stochastic Modelling (or rather the Romanian equivalent).
In the paper we discussed the stochastic modelling and simulation of an action consisting of random events which appear in time and space, with a similarity to a neurophysiological experiment.
Stochastic modelling is applied to identify and characterize event sequences, and to identify temporal dependencies in event sequences.
Because the overall object of the book is to introduce the concepts of stochastic modelling to students and researchers, it is well suited for readers comfortable with graduate-level statistics and mathematics.

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