({{, (RefLink[AssumeDeterministic, paclet:ref/AssumeDeterministic])
is an option for functions such as (RefLink[BayesianMinimization, paclet:ref/BayesianMinimization]) that specifies whether or not the function being considered should be assumed to be deterministic.}})

OpenersNotesSection(Spacer1)Details

With (RefLink[AssumeDeterministic, paclet:ref/AssumeDeterministic])->(RefLink[True, paclet:ref/True]), the function f being considered is assumed to be deterministic, so that f[conf] always gives the same value for a given configuration conf.

With (RefLink[AssumeDeterministic, paclet:ref/AssumeDeterministic])->(RefLink[False, paclet:ref/False]), the function f being considered is assumed to be not necessarily deterministic, so that f[conf] can give different values on different evaluations when the configuration conf is the same.

The typical default setting is (RefLink[AssumeDeterministic, paclet:ref/AssumeDeterministic])->(RefLink[False, paclet:ref/False]).

OpenersPrimaryExamplesSection(Spacer1)Examples  (2)

OpenersExampleSection0(Spacer1)Basic Examples  (1)

Minimize a function over a region using (RefLink[BayesianMinimization, paclet:ref/BayesianMinimization]) with the specification that the function is deterministic:

bo=BayesianMinimization[(# - 2)^2 + 1&,Interval[{0,4}], AssumeDeterministic -> True]

Get the estimated minimum configuration:

bo["MinimumConfiguration"]

Get the estimated minimum value:

bo["MinimumValue"]

For a deterministic function, the model value agrees to good precision with the function value for evaluated configurations:

(# - 2)^2+1&[bo["MinimumConfiguration"]]

OpenersExampleSection1(Spacer1)Scope  (1)

Define a function over a domain defined by a random generator with no specifications:

f[x_]:=-x Sin[x]+3 Cos[x]+x/2;
sampler[]:= RandomReal[{-6., 6.}];

Minimize the function using (RefLink[BayesianMinimization, paclet:ref/BayesianMinimization]) without any specifications:

bo=BayesianMinimization[f, sampler]

Get the minimum configuration and minimum value:

bo["MinimumConfiguration"]

bo["MinimumValue"]

The function is assumed to be stochastic: the model value at the minimum configuration may differ from the function value:

f[bo["MinimumConfiguration"]]

Include information that the function is deterministic:

bo2=BayesianMinimization[ f, sampler, AssumeDeterministic-> True]

Get the minimum configuration and minimum value for the deterministic case:

bo2["MinimumConfiguration"]

bo2["MinimumValue"]

Now the model value and function value at the minimum configuration agree to a good precision:

f[bo2["MinimumConfiguration"]]

({{DocumentationFooterIconsRelatedFunction.png, ({{See Also}, {(RefLink[BayesianMinimization, paclet:ref/BayesianMinimization])  ▪  (RefLink[BayesianMaximization, paclet:ref/BayesianMaximization])  ▪  (RefLink[RandomVariate, paclet:ref/RandomVariate])}})}})