Title: Methods and Applications of Stochastic Modeling
Abstract: 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. Obviously, the natural world is buffeted by stochasticity. But stochastic models are considerably more complicated. 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. It is now being applied in engineering, life sciences, social sciences, and finance. The current work offers a crystal clear discussion regarding the methods and applications of stochastic modeling. This paper mainly focuses on objectives of stochastic model building, criteria for problems of stochastic model building and formulation of stochastic linear regression model. Furthermore assumptions and ordinary least squares (ols) estimation of stochastic linear model have been presented in a lucid manner.
Publication Year: 2021
Publication Date: 2021-09-14
Language: en
Type: book-chapter
Indexed In: ['crossref']
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