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Scoring algorithm : ウィキペディア英語版 | Scoring algorithm
Scoring algorithm, also known as Fisher's scoring,〔(A fast scoring algorithm for maximum likelihood estimation in unbalanced mixed models with nested random effects )〕 is a form of Newton's method used in statistics to solve maximum likelihood equations numerically, named after Ronald Fisher. ==Sketch of Derivation== Let be random variables, independent and identically distributed with twice differentiable p.d.f. , and we wish to calculate the maximum likelihood estimator (M.L.E.) of . First, suppose we have a starting point for our algorithm , and consider a Taylor expansion of the score function, , about : : where : is the observed information matrix at . Now, setting , using that and rearranging gives us: : We therefore use the algorithm : and under certain regularity conditions, it can be shown that .
抄文引用元・出典: フリー百科事典『 ウィキペディア(Wikipedia)』 ■ウィキペディアで「Scoring algorithm」の詳細全文を読む
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