Okeh U M, Ugwu A C
Department Industrial Mathematics and Applied Statistics, Ebonyi State University, Abakaliki, Nigeria.
East Afr J Public Health. 2009 Apr;6 Suppl(1):11-9. doi: 10.4314/eajph.v6i3.45765.
One of the most interesting applications of the results of probability theory involves estimating unknown probability and making decisions on the basis of new (sample) information. Biomedical scientists often use the Bayesian decision theory for the purposes of computing diagnostic values such as sensitivity and specificity for a certain diagnostic test and from which positive or negative predictive values are obtained in other to make decisions concerning the well-being of the patient. Often times error rates are encountered and estimated from the results of trials of the screening test with a view to calculating the overall case rate for which an accurate estimate is rarely available. The concept of conditional probability takes into account information about the occurrence of one event to predict the probability of another event. It is on this premise that this article presents Bayes' theorem as a vital tool. A brief intuitive development of this theorem and its application in diagnosis is given with minimum proof and examples.
概率论结果最有趣的应用之一涉及估计未知概率，并根据新的（样本）信息做出决策。生物医学科学家经常使用贝叶斯决策理论来计算某些诊断测试的诊断价值，如敏感性和特异性，并从中获得阳性或阴性预测值，以便就患者的健康状况做出决策。通常会遇到错误率，并根据筛查测试的试验结果进行估计，以计算总体病例率，但很少能得到准确的估计值。条件概率的概念考虑了一个事件发生的信息来预测另一个事件的概率。正是基于这一前提，本文将贝叶斯定理作为一个重要工具进行介绍。本文以最少的证明和示例对该定理进行了简要直观的推导及其在诊断中的应用。
