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.
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.
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.
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.
DFA - deterministic finite automata NFA - non-deterministic finite automata
monte carlo simulation is used to give solutions of deterministic problems whereas stochastic simulation is used for stochastic problems.
I don't know the answer I am looking for the answers too. :) I'm only 41.
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A deterministic sequence - as opposed to a stochastic or random sequence.
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.
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.
Oh, dude, it's like asking the difference between a taco and a burrito - they're both delicious, but they have their own unique flavors. Stochastic is more about randomness and unpredictability, while probabilistic is all about calculating probabilities and likelihoods. So, like, stochastic is the wild card at the party, and probabilistic is the one crunching numbers in the corner.
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.
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
H. Marcus-Roberts has written: 'Comparison of some deterministic and stochastic models of population growth'