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With the advent of modern technologies, it is increasingly common to deal with data of large dimensions in various scientific fields of study. In this paper, we develop a Bayesian approach for the classification of multi‐subject high‐dimensional ...

Probabilistic models are conceptually powerful tools for finding structure in data, but their practical effectiveness is often limited by our ability to perform inference in them. Exact inference is frequently intractable, so approximate inference is ...

Patterns with pleasing structure are common in art, video games, and virtual worlds. We describe a method for synthesizing new patterns of tiles on a regular grid that are similar in appearance to a set of example patterns. Exemplars are used both to ...

We consider posterior sampling in situations where data are incomplete in such a way that likelihood functions corresponding to portions of the data must be constructed. Slice sampling, a recently developed Markov chain Monte Carlo method, makes such ...

We provide an exact simulation algorithm that produces variables from truncated Gaussian distributions on ( \Bbb R ₊) ^p via a perfect sampling scheme, based on stochastic ordering and slice sampling, since accept-reject algorithms like the one of ...