What are some examples of deterministic and non deterministic algorithm in statistics?
A deterministic algorithm is deterministic. If you give me some inputs, I can tell you exactly what the algorithm will output (or at least that it will be consistent) no matter how many times you rerun the algorithm.Simple gradient descent is a good example. Given a slope function, a starting point, and a number of iterations, the answer will always be the same for well-defined functions.A non-deterministic algorithm is the opposite of this.Non-deterministic algorithms are tough to find in nature, but one good example lies in quantum mechanics. We can't determine certain properties of identical subatomic particles without measuring them. That is to say, given the exact same input, we still don't know what the output will be.There are plenty of non-deterministic algorithms out there. At the time of writing this, however, I'm not recalling any easily explained *statistics* algorithms that are non-deterministic. Maybe someone else can chime in with that.
What is an example of a Deterministic Model?
if it means the same thing in programming and design as it does in mathematics, a deterministic model is usually the contrast to the stochastic model; and a stochastic model is simply one that has one or more random variables in it. so a deterministic model is one in which there are no random variables. random variables are used to represent unknown (non-deterministic) elements of a system. so like in psychology, "if you can name it, you can talk abou it." so the usually problem analysis process involves identifying the variables involved in a process, then determining whether those variables are deterministic or stochastic. so anytime you use an equation, whether it be a car company modeling their anti-lock braking system, or the phone company determining amount of manpower needed to service all the lines in the community, or Microsoft, Dell or Apple determining the percentage of total sales represented by the government, commercial and private individuals, depending on what factors you include in your equation, the resulting model can be deterministic or stochastic.
What are some examples of deterministic forces?
Different environs have different deterministic forces. The single greatest macro force is a natural catastrophe. Human politics follows close behind. Environs also have a hierarchical relationship to one another which controls their relative effects. A drought in the Sudan has little effect on the price of grain in America, however, the price of oil in Arabia affects all the prices for everything across the globe. Then there are the natural forces like gravity, Van Der Waals, entropy, enthalpy, Thermodynamics, quantum mechanics, QED and etc.
Example of a deterministic algorithm?
You see, you have to specify the problem For which problem, you want deterministive algorithm. We have classification deterministic algorithm. We have deterministic algorithm for graph coloring Which problem you want deterministic algorithm. For example, Newton Raphson method to find roots also a deterministic algorithm. LU decomposition is also deterministic approach for solving simultaneous equations.
Can we say that “Orch-OR is a deterministic quantum model”?
Yes, that is the proposal. Deterministic.
Examples of deterministic finite automata (DFA) and non-deterministic finite automata (NFA)
First place to look is always Wikipedia: http://en.wikipedia.org/wiki/Finite_stat... http://en.wikipedia.org/wiki/Nondetermin...
What is an example of a non-deterministic system?
Reality is deterministic only within stated life-span, conditioned, statistically relevant.Nothing in physical life is either permanent, or perfect. It may fall within parameters, and calculated operational variables.
How do we know if a model is deterministic or stochastic in nature? Is there an easy (I am weak at math) way to convert a deterministic model to a stochastic one?
You should not “know” if the model is deterministic or stochastic since it is your empirical decision to choose either.The easiest way to extend a deterministic model to a stochastic model is to add a random noise to the deterministic model.In reverse, you can subtract the deterministic part from a model do check if it was stochastic or not.If the result is white noise (i.i.d. values) then the initial model was stochastic (attached plot).