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関連する概念動画

Survival Tree01:19

Survival Tree

369
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
369
Bootstrapping01:24

Bootstrapping

785
The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is...
785
Classification of Systems-I01:26

Classification of Systems-I

533
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
533
Classification of Systems-II01:31

Classification of Systems-II

445
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
445
Aggregates Classification01:29

Aggregates Classification

947
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
947
Prediction Intervals01:03

Prediction Intervals

3.1K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
3.1K

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関連する実験動画

Updated: Jan 7, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

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TAN-FGBMLE：連続値分類のための高速生成ブートストラップ最尤推定に基づくツリー拡張ナイーブベイズ構造学習

Chenghao Wei1,2, Tianyu Zhang1,2, Chen Li1,2

  • 1School of Computer Science, Hubei University of Technology, Wuhan 430068, China.

Entropy (Basel, Switzerland)
|December 24, 2025
PubMed
まとめ

高速生成ブートストラップ最尤推定（TAN-FGBMLE）を使用したツリー拡張ナイーブベイズ（TAN）構造学習のための新しい方法を開発しました。このアプローチは、連続属性の密度推定を改善し、モデルの精度と解釈可能性を高めます。

キーワード:
ツリー拡張ナイーブベイズブートストラップクラス条件付き相互情報量複雑な密度推定生成モデル最尤推定

関連する実験動画

Last Updated: Jan 7, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

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