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Updated: Sep 8, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Published on: July 3, 2020

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修正された一般線形モデルに期待-最大化アルゴリズムを適用することによって,ノイズから生物学的なバリエーションを分離する

Tien-Wen Lee1

  • 1The NeuroCognitive Institute (NCI) Clinical Research Foundation, Mount Arlington, New Jersey, USA.

Journal of computational biology : a journal of computational molecular cell biology
|September 5, 2025
PubMed
まとめ

新しい方法であるEMSEVは,一般的な線形モデル (GLM) のノイズから生物学的バリエーションを区別します. この統計的アプローチは,先天的な生物学的変動をランダムなノイズから分離することによって,生物学的データ分析を改善します.

キーワード:
設計マトリックス期待の最大化アルゴリズム一般的な線形モデルグローバル オプティム地元の最適値

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