Speaker: Will Bishop
Abstract: I’ll be presenting some of my recent work on statistical matching - the problem of estimating a joint distribution or properties of it when many pairs of random variables are never sampled simultaneously. It is well established that statistical matching encounters problems of non-identifiability unless certain assumptions, such as conditional independence of any pair of variables never sampled together, hold. In this work, we reexamine the conditions under which models can be identified in the statistical matching scenario. We show that latent variable models, such as factor analysis and latent trait models, while violating the typical conditional independence assumptions appealed to in statistical matching, are nonetheless, generically identifiable. Intuitively, generic identifiability establishes identifiability for sets of randomly generated parameters, and we present conditions establishing when latent variable models are generically identifiable and unidentifiable in the statistical matching scenario. I’ll also spend some time describing real neuroscience analyses which motivate the use of statistical matching. This is joint work with Byron Yu and Geoff Gordon that we are currently writing up (so no paper yet), and we would love to get your feedback.