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Date: 18-5-2016
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Date: 6-3-2016
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Date: 15-9-2020
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Adaptive Kernel Density Estimators
This technique combines the data-adaptive philosophy (the philosophy of varying the bin width) with the kernel approach (Silverman 1986: 100-110). The intent, of course, is to gain the advantages of both. A necessary first step is to get some rough idea of the local density around each datum point. Almost any estimator works for such a pilot estimate; the standard kernel estimator with fixed bin width is a common choice. Next, assign a bin width to each datum point, tailoring that width to the local density as given by the pilot estimate. Then choose a kernel (e.g. Fig. 1b) and estimate the entire probability distribution in a way essentially like that described above for the kernel estimator. Authors have suggested ways to fine-tune one or more aspects of the procedure.
Figure 1: Basic concepts of kernel probability estimation. (a) Hypothetical probability density (the entire curve) for a group of measurements, showing bin width ε, data point x on which a bin is centered, and a neighboring point xi. = (b) Popular kernels (assumed local probability distributions).
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تجربة بسيطة مدتها 3 دقائق تحسن النظر كثيرا
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الصين.. تطوير محفز نانوي رخيص الثمن لتنقية المياه من النترات
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بحضور علمائي ورسمي .. العتبة العلوية المقدسة ترفع راية الحزن والحداد إيذاناً بحلول شهر محرم الحرام
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