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 ?
1) can be extended to consider stochastic model reliability by rewriting the left hand side as [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] and averaging it over f (P([G.
2]/2, then the total population of x(t) and y(t) of stochastic model system (2) starting from any interior point of first quadrant is strongly persistent in mean.
s model (1995) along with a budgetary constraint where the demand follows several stochastic models.
A stochastic model defines relationships and, presumably, leads over time (with the gathering of data) to a heuristic model, which fine-tunes the relationships and permits accurate predictions.
Our stochastic model showed that high exposure to the pathogen from the reservoir host can give the appearance of endemic infection in the target host, even if it cannot sustain the pathogen alone.
5] concentrations obtained by the stochastic models were in the same range as the measured concentrations, whereas the N[O.
In this current study, the stochastic model was adapted to predict shrinkage and degrade distributions.
Given the connection between geometric and negative binomial distributions, applications of these stochastic models hinge on characteristics of a specific setting.
In the following section, we introduce the stochastic model advanced by Lotka (1926) as a possible description of the superstar generating process.
While the second stochastic model is in the spirit of synthetic valuation, the third market model is definitely in the spirit of the market-segmentation hypothesis.
Genetic, epidemiologic and laboratory studies support a stochastic model of breast cancer development in which a series of genetic changes contribute to the dynamic process known as carcinogenesis.

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