Bayesian Belief Networks are networks of connected variables that generate predictions based on assumptions. These are generally used when there is a lack of data available.
Bayesian Belief Networks – Example
Let’s say we are determining the likelihood of a person owning a Michael Kors handbag and the average UK person has a likelihood of 20% of owning one. If we know some additional pieces of information, we can update our model based on this. We can then use this to determine the specific probability of owning a Michael Kors handbag using the information we currently know.
The Average Joe has a 20% chance of owning a Michael Kors handbag
Person A: 1) Owns a dog, 2) Is male, 3) Has a semi-detached house
Person B: 1) Owns a Michael Kors watch, 2) Is aged 20-25, 3) Is female
Person C: 1) is aged 40-45, 2) Shops in Prada 3) Wears Chanel perfume
The likelihood that these users own a Michael Kors handbag is different for each individual based on the variables that are known because the statistical links are measured between these types of variables.
With Bayesian modelling, we can update the likelihood that a user owns a handbag despite the sparse details available.
With the known information, the model uses this to predict the likelihood of owning a Michael Kors handbag vs. not owning one, despite the fact there are certain elements about the user we do not know.
Bayesian Belief Networks are part of the various advanced analytics techniques we use in our software solutions to derive meaningful insights into consumers. Our solutions PROFILE and Discovery help businesses to personalise marketing to boost ROI.
Check out our blog on Natural Language Processing