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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.

Q: What is stochastic testing?

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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.

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

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

the output will be different everyime

Nope

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Stochastic Models was created in 1985.

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

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.

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

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

You can download free copy of Stochastic Filtering by Ramaprasad Bhar here. /forexebooks.co.in/stochastic-filtering-applications-finance-ebook/

Hiroaki Morimoto has written: 'Stochastic control and mathematical modeling' -- subject(s): Stochastic control theory, Optimal stopping (Mathematical statistics), Stochastic differential equations

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.

stochastic demand is random demand. it is determined by predictable actions and a random element.

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

Kurt Marti has written: 'Stochastic Optimization' 'Coping with uncertainty' -- subject(s): Mathematical models, Uncertainty 'Computation of efficient solutions of discretely distributed stochastic optimization problems' 'Descent directions and efficient solutions in discretely distributed stochastic programs' -- subject(s): Stochastic processes, Mathematical optimization