Stopped decision processes on complete separable metric spaces

Nonparametric Priors on Complete Separable Metric Spaces

A Bayesian model is nonparametric if its parameter space has infinite dimension; typical choices are spaces of discrete measures and Hilbert spaces. We consider the construction of nonparametric priors when the parameter takes values in a more general functional space. We (i) give a Prokhorov-type representation result for nonparametric Bayesian models; (ii) show how certain tractability proper...

Separable Fuzzy Metric Spaces

Many authors have introduced the concept of fuzzy metric spaces in different ways.In this paper, we get a special structure for some separable fuzzy metric and M-fuzzy metric spaces, where the metric space has been introduced by George and Veermani.

Solving Markov Decision Processes in Metric spaces

We present an approximation scheme for solving Markov Decision Processes (MDPs) in which the states are embedded in a metric space. Our algorithm has a time bound of Õ(n log logn ), where n is the number of states and the approximation factor. This bound is independent of the numerical size of the input and the discount factor and is hence strongly polynomial. We present the result for determin...

On π-images of separable metric spaces

We prove that a space is a sequentially-quotient (pseudo-sequencecovering), π-image of a separable metric space if and only if it has a point-star network consisting of countable-cs∗-covers. We also investigate spaces with count-

On Generalized Complete Metric Spaces