Hi, I am using a set of data that I collected using biosensors, the goal is to analyze the data and to infer the dependencies between the variables to construct a bayesian network that infers the mood of a person (e.g. happy, sad...etc). Clearly, the response variable is non-ordinal and categorical. The model should not include interdependent sensors. I would like to get some thoughts about using regression for such a task, is it a good choice ? are there any other possible techniques to use? More importantly, would it be possible to automate the building of such models? Is anyone aware of any earlier work in that area?. Thanks. Fahd
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