Question

What is the relationship between Naïve Bayes and Bayesian networks? What is the process of developing a Bayesian networks model?

Answer 1

1. Relationship between Naive Bayes and Bayesian networks:

> Naive Bayes assumes that all the features are conditionally independent of each other. This therefore permits us to use the Bayesian rule for probability. Usually this independence assumption works well for most cases, if even in actuality they are not really independent.
> Bayesian network does not have such assumptions. All the dependence in Bayesian Network has to be modeled. The Bayesian network formed can be learned by the machine itself, or can be designed in prior, by the developer, if he has sufficient knowledge of the dependencies.

2. Steps for developing a Bayesian networks model:

(a). Expert driven identification of model variables which are considered to be
important for estimating the risk of violent re-offence.
(b). Expert constructed causal model structure based on the variables identified at the above step.
(c). Link relevant data to model variables.
(d). Perform model parameterisation and use the Expectation Maximisation algorithm to deal with missing data;
(e). Experts review the resulting behaviour of the model and suggest further revisions where necessary.