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These words are used to describe ways of modeling or understanding the world. "Stochastic" means that some elements of the model or description are thought of as being random. (The word "Stochastic" is derived from an ancient Greek word for random.) A model or description that has no random factors, but conceivably could, is called "deterministic."
For example, the equation
Q = VC
where Q = charge, V = voltage, and C = capacitance, is a deterministic physical model. One stochastic version of it would be
Q = VC + e
where e is a random variable introduced to account for or characterize the deviations between the actual charges and the values predicted by the deterministic model.
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What is the difference between Monte Carlo and stochastic methods of simulation?
monte carlo simulation is used to give solutions of deterministic problems whereas stochastic simulation is used for stochastic problems.
How would you explain to an idiot what 'Stochastic Processes' are?
Buying a lottery ticket daily is deterministic. Winning a lottery and getting a prize is Stochastic.
What is the difference between stochastic and random?
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.
What is the difference between the stochastic error term and the residual?
Ah, the stochastic error term and the residual are like happy little clouds in our painting. The stochastic error term represents the random variability in our data that we can't explain, while the residual is the difference between the observed value and the predicted value by our model. Both are important in understanding and improving our models, just like adding details to our beautiful landscape.
What is full form of DFA and NFA?
DFA - deterministic finite automata NFA - non-deterministic finite automata
