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gguioLG / Graph-DB-for-Smart-AI-ML

Amassing competence on using ML & AI on top of Graph DB
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Build brief explanation of Bayesian networks (For Virtual team) #8

Open gguioLG opened 6 years ago

gguioLG commented 6 years ago

Brief overview on Bayesian Networks.

Bayesian networks reflects the states of some part of a world that is being modeled and it describes how those states are related by probabilities.

BN is a powerful Machine learning technique as it allows the user to carry out inference on data. The users is also able to see cause and effects.

Absolutely anything can be modeled by a Bayes net. As with any other network, the graph of BN is made up of nodes that have states and the possible states of the model represent all the possible worlds that can exist

The links between any two nodes indicate that there are probability relationships that are known to exist between the states of those two nodes. The direction of the links corresponds to causality

Bayes Rule: For any two events, A and B, p(B|A) = p(A|B) x p(B) / p(A) Below is an example of what a Bayes Net would look like. bn

For more information, visit this site: https://www.norsys.com/tutorials/netica/secA/tut_A1.htm#WhatIsABayesNet

mRaafay commented 6 years ago

BAYESIAN NETWORKS_inforgraphic.pdf

An infographic, brief explanation on BN and recommended software's and packages to be used.