Dynamic Naïve Bayes
Dynamic Naive Bayes (DNB) is an algorithm devoted to detect and cluster the complex cause-effect relationships in discrete dataset representing a multivariate temporal process.
DNB algorithm is composed of three parts:
- Equations to detect the local cause-effect relations between the variables in the temporal flow;
- Equations to establish the global and prevalent cause-effect relations between the variables in the whole temporal multivariate process;
- Equations to select the most likelihood cause-effect relations between variables at different scale of time (Multi-Scale Entropy – MSE), in order to cluster them and to project them in direct graph, sparse or connected.