(New page: {{StatsPsy}} {{Expert}} '''Stochastic modeling''' is a statisical technique. "Stochastic" means being or having a random variable. A stochastic model is a tool for estimating probabili...)

'''Stochastic modeling''' is a [[statisical]] technique. "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).

'''Stochastic modeling''' is a [[statisical]] technique. "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).

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Its application initially started in physics (sometimes known as the [[Monte Carlo Method]]). It is now being applied in the life sciences and social sciences.

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Its application initially started in physics (sometimes known as the Monte Carlo Method). It is now being applied in the life sciences and social sciences.

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Stochastic modeling is a statisical technique. "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 (sometimes known as the Monte Carlo Method). It is now being applied in the life sciences and social sciences.