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 ?
Passenger flow study has given rise to the stochastic modeling and statistical estimation of fine individual passenger travel phenomena by trip leg or in station in rail transit system, a closed black-box.
Frederiksen and Kepert (2006) then used the functional form of these closure approaches to develop a zero-parameter stochastic modeling framework, where the eddy viscosities are determined from higher-resolution reference simulations.
Stochastic Modeling of Scientific Data', First Edition, Chapman and Hall 1995.
Their topics include the design and verification process, block diagram modeling and system analysis, multiple domain modeling, statistical and stochastic modeling, design flow, and a complex electronic system design example.
Thus, we can conclude that stochastic modeling provides a more accurate prediction in finding out the expected time to extinction of infected population and hence disease pathogenesis.
How does it differ from regular stochastic modeling and Monte Carlo simulations?
In fact, the author states that he hopes that readers are "able to use this book as a stepping stone to learn more advanced topics in stochastic analysis." Those who are interested in using stochastic modeling as a tool for pricing financial and insurance products are likely to find the book a helpful resource for a wide variety of issues.
Also, insurers are using analytics on the investment side, using stochastic modeling In many of their hedging and investment operations.
The topics include: suspensions, bubble and drop dynamics, flow in porous media, interfaces, turbulent flow, injectors and nozzles, particle image velocimetry, macroscale constitutive models, large eddy simulation, biological flows, environmental multiphase flow, and phase changes and stochastic modeling, among others.
* Deterministic modeling to stochastic modeling: Gone are the days when deterministic modeling was acceptable to solve supply chain problems.
Stochastic modeling involves use of computer simulations to determine how a product or company might perform under a wide range of randomly changing conditions.
In addition to answering these capital questions, many other risk management issues can be addressed with stochastic modeling. For example, even though bootstrap modeling is primarily focused on the liability (reserve) risk portion of required capital, it can also provide valuable insights into the pricing risk portion of required capital (since it simulates historical triangles in addition to the possible future outcomes).

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