A Stochastic Approximation Method
Метод стохастической аппроксимации
1951-09-01
SCID: 54.1/bhgjcwvj
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convergence in probabilitymonotone response functionroot-findingstochastic approximationsuccessive experiments
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Abstract (AI)
Let $M(x)$ denote the expected value at level $x$ of the response to a certain experiment. $M(x)$ is assumed to be a monotone function of $x$ but is unknown to the experimenter, and it is desired to find the solution $x = \theta$ of the equation $M(x) = \alpha$, where $\alpha$ is a given constant. We give a method for making successive experiments at levels $x_1,x_2,\cdots$ in such a way that $x_n$ will tend to $\theta$ in probability.
Key Findings
1
Assumes only that the response expectation M(x) is monotone, while its functional form is unknown to the experimenter.
2
Introduces a stochastic approximation method for locating the unknown solution θ of M(x)=α through sequential experiments.
3
Provides a sequential approach for estimating a target level defined by an expected experimental response.
4
Specifies adaptive experiment levels x₁, x₂, … designed so that the sequence xₙ converges in probability to θ.
Research Object
The unknown monotone response function M(x) of the experiment and its target level θ satisfying M(θ)=α
Research Subject
The probabilistic convergence of successive experimental levels x_n to the solution θ of M(x)=α
Publication Details
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1951-09-01
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