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Thursday, September 12, 2013

Bayesian Belief Networks for inference and learning

I have attended a daylong seminar at IIM Bangalore on 10th September on the subject of Bayesian Belief Networks. Dr. Lionel Jouffe, gave 4 case studies during the one day technical session.

Introduction to Bayesian Belief Networks (BBNs) and building the network with multiple modes i.e., a network is built from a. mining the past data, or b. built purely from expert knowledge capture or a combination of both methods. Once the conditional probabilities for each node exist and associations between the nodes are built, both assisted and non-assisted learning can be used.

First case study involved knowledge discovery in the stock market whereby loading publicly available stock market data, a BBN is built, automatic clustering algorithm using the 'discritized' continuous variables was run to find similar tickers. ( http://www.bayesia.us/index.php/knowledgediscovery )

Second case study showed was on segmentation using BBNs. Input contained market share of 58 stores selling juices. Three groups of juices like local brands, national brands and premium brands of juices are sold in one state of US across 11 brands of above three groups. Using this data, BBN was built, automatic segmentation performed into 5 segments with a good statistical description of segmentation.

Third case study involved a marketing mix analysis to describe and predict the efficiency of multiple channel campaigns (like TV, radio, online) on product sales. ( http://www.bayesia.us/index.php/mktgmix )

The fourth case study covered a vehicle safety scenario taking publicly available accident data to discover the two key factors that can reduce fatality of injury based on parameters of vehicle, driver etc., ( http://www.bayesia.us/index.php/vehiclesafety )


Conclusion is any analytic problem can be converted into a BBN and solved. I have seen few advantages of this approach:
1. The BBN can be built in a No Data scenarios with expert knowledge completely hand crafted. It can also be built from big data scenario deriving the conditional probabilities mining the data.
2. One strong theoretical framework solving the problems making it easier to learn. No need to learn multiple theories.
As a technique, it has some promising features. The whitepapers presented are useful in understanding the technique in different scenarios. Views? Comments?
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