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Why I think Bayesianism is neat and cool

July 30, 2013
Rev. Thomas Bayes

Rev. Thomas Bayes

 Last month I went to a Bayesian Networks (BN) training conducted by Bayesian Intelligence.
Let us say you want to study the relationships of several random variables. Using BN you can represent their relationships by accounting for the conditional dependencies that they may have using a directed acyclic graph (DAG).
There is something subtle about BNs and yet efficiently slick and powerful.  It is intuitive and visual, something easy to communicate to high level decision makers. It can capture the thing you are modelling in a very concise form.
What is really great about this is that you do not have to fall into heavy mathematical or statistical machinery to model the system of random variables that are under your study.
This to me is the best aspect of BNs, it is modelling without tears, without heavy duty maths or stats. It cuts to the chase. The draw back of course is the assigning of probabilities when things get complex but that is rather a trivial and tedious exercise compared to building a maths/stats model that has to rely on complex PDEs and what have you etc. From a modeller’s point of view specially when uncertainty is present (which is the case in life) BNs are not hard to like.
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