The definition to the term "Stochastic Process" is: A statistical process involving a number of random variables depending on a number variable. Which in most cases, is time.
Stochastic means non-deterministic. This means that something contains an inherent degree of randomness. For more detail, you should consult a dictionary or more detailed literature on probability theory.
Mathematical model is exact in nature.it has Beta zero and Beta one and no stochastic or disturbance variables. Econometric model represents omitted variable, error in measurement and stochastic variables.
A Stochastic error term is a term that is added to a regression equation to introduce all of the variation in Y that cannot be explained by the included Xs. It is, in effect, a symbol of the econometrician's ignorance or inability to model all the movements of the dependent variable.
A stochastic error is a type of random error that occurs in statistical models or experiments. It is caused by factors that are unpredictable or beyond the control of the researcher, leading to variability in the data. Stochastic errors can be minimized through larger sample sizes or by using statistical techniques to account for their presence in the analysis.
Regression analysis is based on the assumption that the dependent variable is distributed according some function of the independent variables together with independent identically distributed random errors. If the error terms were not stochastic then some of the properties of the regression analysis are not valid.
no, it is a random process
Stochastic process is also known as a random process. It is a collection of random variables that represent the evolution of some system of random values over time.
The mathematical theory of stochastic integrals, i.e. integrals where the integrator function is over the path of a stochastic, or random, process. Brownian motion is the classical example of a stochastic process. It is widely used to model the prices of financial assets and is at the basis of Black and Scholes' theory of option pricing.
Wikipedia states that stochastic means random. But there are differences depending on the context. Stochastic is used as an adjective, as in stochastic process, stochastic model, or stochastic simulation, with the meaning that phenomena as analyzed has an element of uncertainty or chance (random element). If a system is not stochastic, it is deterministic. I may consider a phenomena is a random process and analyze it using a stochastic simulation model. When we generate numbers using a probability distribution, these are called random numbers, or pseudo random numbers. They can also be called random deviates. See related links.
Stochastic testing is the same as "monkey testing", but stochastic testing is a lot more technical sounding name for the same testing process. Stochastic testing is black box testing, random testing, performed by automated testing tools. Stochastic testing is a series of random tests over time. The software under test typically passes the individual tests, but our goal is to see if it can pass a large number of individual tests.
A. K. Basu has written: 'Introduction to Stochastic Process' 'Marketing Research'
Tapio Westerlund has written: 'Application of stochastic control theory to the dry cement manufacturing process' -- subject(s): Cement industries, Production control, Stochastic control theory
Stochastic Models was created in 1985.
G. Adomian has written: 'Stochastic systems' -- subject(s): Stochastic differential equations, Stochastic systems
When a person uses the term stopped process in Mathematics it means that a person is using a stochastic process or assuming the value of time after it was prescribed or given.
C. W. Gardiner has written: 'Handbook of Stochastic Methods' 'Stochastic methods' -- subject(s): Stochastic processes 'Quantum noise' -- subject(s): Stochastic processes, Quantum optics, Josephson junctions
Quan-Lin Li has written: 'Constructive computation in stochastic models with applications' -- subject(s): Stochastic processes, Stochastic models