Bayesian nets and medical diagnosis. A different way to learn conditional probabilities

Modelling in Science Education and Learning

Francisco J. Boigues¹  Vicente Domingo Estruch Fuster¹  José I. Pastor¹  Anna Vidal¹ 

[1] Universitat Politècnica de València;

关键词: redes bayesianas;    teorema de bayes;    modelización matemática;    diagnóstico médico;   

DOI  :  10.4995/msel.2019.10830

来源: DOAJ

【 摘 要 】

Bayesian networks are a formal tool that allows to model processes characterized by uncertainty, which is typical of many real problems. A Bayesian network can establish a comprehensive model on a set of random variables and their relationships. This model can be used to estimate probabilities of certain variables of the network, which are called state variables, when other variables, named evidence variables, are fixed. The process of obtaining the probability distribution of the state variables, when the evidences are fixed, is named Bayesian probabilistic inference. In this work, after introducing the Bayesian networks, it is exposed how to use them in the classroom analysing medical diagnosis problems. This leads to a more meaningful learning of the concepts of independence and conditional probability, which are essential for the correct application of numerous probabilistic and statistical methods.

【 授权许可】

Unknown