Bayesian data analysis is the art of extracting information from data using Bayesian methods. There are three main aspects of data analysis

• model building
• model refinement
• model validation

Model building: I use graphical models and Bayesian networks for modeling complex dependency structures.

Model refinement: This is the task of refining a class of models using data. My expertize is in developing methods for automating this process. Methods I developed for automated learning with incomplete data are implemented in Discoverer and RoC. I am currently developing a greedy search algortihm for inducing Bayesian networks from continuous data.

Model validation: This is the task of validating a model refined from data. My current interest is in developing diagnostic tools to assess the goodness of Bayesian networks learned from data.