• Title, Summary, Keyword: 추리통계학

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Discussion for Ride Evaluation of High Speed Train by Using Inferential Statistics (추리통계학을 이용한 고속철도 승차감 평가에 대한 고찰)

  • Hwang, Hee-Soo;Kim, Seog-Won;Park, Chan-Kyeong;Mok, Jin-Yong;Kim, Ki-Hwan;Kim, Young-Guk
    • Journal of the Korean Society for Railway
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    • v.11 no.6
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    • pp.543-549
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    • 2008
  • The ride comfort is more important according to train speedup. Generally it is defined as the vehicle vibration. There are many studies on evaluation method of ride comfort for railway. But the ride comfort for Korean high speed train (HSR 350x) has been assessed by statistical method according to UIC 5l3R. In this paper, the ride indices, which were measured in the Korean high speed train. have been analyzed and reviewed by using the inferential statistics such as t-test, variance analysis (ANOVA) and regression analysis.

Causal reasoning studies with a focus on the Power Probabilistic Contrast Theory (힘 확률 대비 이론에 기반을 둔 인과 추론 연구)

  • Park, Jooyong
    • Korean Journal of Cognitive Science
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    • v.27 no.4
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    • pp.541-572
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    • 2016
  • Causal reasoning is actively studied not only by psychologists but, in recent years, also by cognitive scientists taking the Bayesian approach. This paper seeks to provide an overview of the recent trends in causal reasoning research with a focus on the power probabilistic contrast theory of causality, a major psychological theory on causal inference. The power probabilistic contrast theory (PPCT) assumes that a cause is a power that initiates or inhibits the result. This power is purported be understood through statistical correlation under certain conditions. The paper examines the supporting empirical evidence in the development of PPCT. Also, introduced are the theoretical dispute between the PPCT and the model based on Bayesian approach, and the current developments and implications of research on causal invariance hypothesis, which states that cause operates identically regardless of the context. Recent studies have produced experimental results that cannot be readily explained by existing empirical approach. Therefore, these results call for serious examination of the power theory of causality by researchers in neighboring fields such as philosophy, statistics, and artificial intelligence.