We present a general framework for defining priors on model structure and sampling from the posterior using the Metropolis-Hastings algorithm. The key ideas are that structure pri...
Abstract. We present a new classification algorithm that combines three properties: It generates decision trees, which proved a valuable and intelligible tool for classification an...
Many perception, reasoning, and learning problems can be expressed as Bayesian inference. We point out that formulating a problem as Bayesian inference implies specifying a probabi...
We propose efficient particle smoothing methods for generalized state-spaces models. Particle smoothing is an expensive O(N2 ) algorithm, where N is the number of particles. We ov...
Mike Klaas, Mark Briers, Nando de Freitas, Arnaud ...
There has been much interest in the recent past concerning the possibilities for automated categorization of named entities. The research presented here describes a method for the...