Fahd: What you propose could be done quite simply using a single-layer neural network. Treat each sensor as either a binary response variable, or some type of continuous value for more complex sensors. Weight the inputs from each of these sensors and assign some threshold function that translates weighted combinations of these inputs to your categorical outputs of interest. Then, have the network 'learn' the relationship between the inputs and outputs.
On the other hand, if you are sure you want to use Bayesian approach, then you might try the following way of thinking. Recall: P(model | data) = P(data | model) P(model) / P( data ) represents the posterior probability given prior information (a set of beliefs), evidence (observations), and a likelihood (the model). List out your inputs and the outputs, and draw a directed acyclic graph to represent the 'network' you are interested in, to help you think about the problem. The evidence, P(data), is computed by taking the outputs of each of the sensors and computing the probability of THAT specific combination of outputs over all possible combinations of outputs. Assume 'model' here represents the relationship between the 'mood' of the person as inferred from the biosensors. P(model) would be the prior probability of that person being in any particular mood. If this is a time-dependent network, you can compute the prior for this based on recent historical data; but if this is a static case, then you can choose to use an uninformative prior, or you may also assume that it is more likely that the person is in a 'happy mood' some % of the time, which gives you an informed prior. P(data | model) is the likelihood of seeing a particular set of outputs from the sensors given that the person is in a particular mood. P(model | data) is the posterior probability you are trying to estimate, that is, the probability that the person is in a specific 'mood' based on the available data from biosensors. There are many useful tutorials out there on the web to get you started with Bayesian networks. You can look into the various literature in the Machine Learning community to get you started. Some of the work by Ghahramani is very useful in this respect, as he has worked out the similarities and relationships between various types of models. Google it. I'm not sure why you would need to do a 'regression analysis' if you're using the bayesian strategy; but then again, i'm not sure you know which approach you are actually interested in. hope that helps... P . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
