LATENT REFERENCES / TAG2
Kernel Density Estimates
Original title: カーネル密度推定値
This reference note belongs to Tag2 in Latent References, an archive curated by Keigo Yoshida. Its archive region is Analysis. The note preserves its source text and links so that readers can trace the material behind the 3D map.
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English translation of the archived note. JP shows the original text. Source links and literal code are retained; the translation does not update or independently verify the source claims.
Kernel density estimation is 1 method of estimating an overall distribution from finitely many sample points.
To estimate a distribution’s density function, methods assuming a parametric model (normal, exponential, gamma distributions, etc.) are used.
When a distribution cannot be described with a parametric model, nonparametric estimation is used. Kernel density estimation is a representative example.
https://scrapbox.io/files/65e723ba7115b60024040ac6.png
#analysis
カーネル密度推定は、有限の標本点から全体の分布を推定する手法の1つです。
ある分布の密度関数を推定したい場合は、パラメトリックモデル（正規分布、指数分布、ガンマ分布など）を想定した手法が使われます。
分布をパラメトリックモデルで記述できない場合は、ノンパラメトリック推定という手法が使われます。カーネル密度推定はノンパラメトリック推定の代表例です。
https://scrapbox.io/files/65e723ba7115b60024040ac6.png
#analysis
Source updated 2024-08-06 · Snapshot 2026-10-08
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Cosine values measure shared lexical features, not truth, agreement or identical meaning. Original reference links are labeled separately.
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- Bonferroni CorrectionComputed lexical cosine similarity 0.211 · shared title, text, tags and references
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- Kaiser CriterionComputed lexical cosine similarity 0.205 · shared title, text, tags and references
- Hilbert SpaceComputed lexical cosine similarity 0.204 · shared title, text, tags and references
- Cronbach’s AlphaComputed lexical cosine similarity 0.202 · shared title, text, tags and references