Hi, I wonder if anyone can give me a handle on this problem. I have sampled 100,000 point from an isometric gaussian in 98 dimensional space (each point translates into a particular face in a "face space" model). I have then defined a uniform distribution over the 100, 000 points. so far so good.
I have then used a kind of decision procedure (making sibjective judgements about which face is most similar to a target face) to update the distribution, such than some points have more mass than others.
What I want to do now is to re-sample from another gaussian, based on this updated distribution. It seems to me that the mean of the new gaussian should be the expectation of the points (i.e. the sum of all the points weighted by their respective probabilities).
Does that seem right to people?
More problematically, for me at least, how can I use the probabilities to estimate a new covariance matrix?
Many thanks for any insights. MD