Q: What is stochastic simulation?

Write your answer...

Submit

Still have questions?

Continue Learning about Math & Arithmetic

monte carlo simulation is used to give solutions of deterministic problems whereas stochastic simulation is used for stochastic problems.

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.

Buying a lottery ticket daily is deterministic. Winning a lottery and getting a prize is Stochastic.

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.

Nope

Related questions

monte carlo simulation is used to give solutions of deterministic problems whereas stochastic simulation is used for stochastic problems.

monte carlo simulation is used to give solutions of deterministic problems whereas stochastic simulation is used for stochastic problems. basically Monte carlo simulation was named after world war -2 by j. von newmann to solve real world problems From - kapil M.tech Student

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.

D stochastic simulation

Any simulation model that does not contain any random or probabilistic element is called a deterministic simulation model. The characteristic of this type of simulation model is that the output is determined when the set of input elements and properties in the model have been specified. For example, a deterministic simulation model can represent a complicated system of differential equations. Many simulation models however, have at least one element that is random, which gives rise to the stochastic simulation model. In most simulation models randomness is important to mimic the real scenario, for example user connections to the internet arise 'randomly' when a person pressing a key. However, for any stochastic simulation model that has random output, the output (numerical results) can only be treated as an estimate of the true output parameters of the model

Joseph A. Fisher has written: 'Object oriented simulation tools for discrete-continuous, stochastic-deterministic simulation models' -- subject(s): Computer simulation, Object-oriented programming (Computer science)

H. M. Scoging has written: 'A stochastic model of daily rainfall simulation in a semi-arid environment' -- subject(s): Mathematical models, Rain and rainfall, Stochastic processes

Haitham N. Yousef has written: 'A PC package for simulation of porous media based on stochastic pore networks'

Brian D. Ripley has written: 'Stochastic Simulation' 'Pattern recognition and neural networks' -- subject(s): Neural networks (Computer science), Pattern recognition systems

Stochastic Models was created in 1985.

Granino Arthur Korn has written: 'Random-process simulation and measurements' -- subject(s): Stochastic processes, Electronic analog computers, Statistical communication theory, Data processing

G. Adomian has written: 'Stochastic systems' -- subject(s): Stochastic differential equations, Stochastic systems