Probabilistic Reasoning in Intelligent Systems is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty. The… (More)

I discuss Bayesian factor regression models with many explanatory variables. These models are of particular interest and applicability in problems of prediction, but also for elucidating underlying… (More)

We describe and develop a close relationship between two problems that have customarily been regarded as distinct: that of maximizing entropy, and that of minimizing worst-case expected loss. Using a… (More)

We investigate directed Markov fields over finite graphs without positivity assumptions on the densities involved. A criterion for conditional independence of two groups of variables given a third is… (More)

We propose a factor model which allows a parsimonious representation of the time series evolution of covariances when the number of series being modelled becomes very large. The factors arise from a… (More)

Gaussian processes are a natural way of specifying prior distributions over functions of one or more input variables. When such a function defines the mean response in a regression model with… (More)

In regression models, such as generalized linear models, there is often substantial prior uncertainty about the choice of covariates to include. Conceptually, the Bayesian paradigm can easily… (More)