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Stopping rule problems are associated with two objects: A sequence of random variables ,, …, whose joint distribution is something assumed to be known; A sequence of 'reward' functions () which depend on the observed values of the random variables in 1:
For example, if the map φ is not surjective, there is no natural way to define such a pushforward outside of the image of φ. Also, if φ is not injective there may be more than one choice of pushforward at a given point. Nevertheless, one can make this difficulty precise, using the notion of a vector field along a map.
In general, any measurable function can be pushed forward. The push-forward then becomes a linear operator, known as the transfer operator or Frobenius–Perron operator.In finite spaces this operator typically satisfies the requirements of the Frobenius–Perron theorem, and the maximal eigenvalue of the operator corresponds to the invariant measure.
With the factor 2 replaced by approximately 2.59, the Freedman–Diaconis rule asymptotically matches Scott's Rule for data sampled from a normal distribution. Another approach is to use Sturges's rule : use a bin width so that there are about 1 + log 2 n {\displaystyle 1+\log _{2}n} non-empty bins, however this approach is not recommended ...
Given a starting position and a search direction , the task of a line search is to determine a step size > that adequately reduces the objective function : (assumed i.e. continuously differentiable), i.e., to find a value of that reduces (+) relative to ().
This rule is also called the oversmoothed rule [7] or the Rice rule, [8] so called because both authors worked at Rice University. The Rice rule is often reported with the factor of 2 outside the cube root, () /, and may be considered a different rule. The key difference from Scott's rule is that this rule does not assume the data is normally ...
The pullback bundle is an example that bridges the notion of a pullback as precomposition, and the notion of a pullback as a Cartesian square. In that example, the base space of a fiber bundle is pulled back, in the sense of precomposition, above. The fibers then travel along with the points in the base space at which they are anchored: the ...
Graphs of probabilities of getting the best candidate (red circles) from n applications, and k/n (blue crosses) where k is the sample size. The secretary problem demonstrates a scenario involving optimal stopping theory [1] [2] that is studied extensively in the fields of applied probability, statistics, and decision theory.