Orbanz, P Projective Limit Random Probabilities on Polish Spaces. Electronic Journal of Statistics, 5. pp. 4-1373. (Unpublished)Full text not available from this repository.
A pivotal problem in Bayesian nonparametrics is the construction of prior distributions on the space M(V) of probability measures on a given domain V. In principle, such distributions on the infinite-dimensional space M(V) can be constructed from their finite-dimensional marginals---the most prominent example being the construction of the Dirichlet process from finite-dimensional Dirichlet distributions. This approach is both intuitive and applicable to the construction of arbitrary distributions on M(V), but also hamstrung by a number of technical difficulties. We show how these difficulties can be resolved if the domain V is a Polish topological space, and give a representation theorem directly applicable to the construction of any probability distribution on M(V) whose first moment measure is well-defined. The proof draws on a projective limit theorem of Bochner, and on properties of set functions on Polish spaces to establish countable additivity of the resulting random probabilities.
|Uncontrolled Keywords:||math.ST math.ST stat.ML stat.TH|
|Divisions:||Div F > Computational and Biological Learning|
|Depositing User:||Unnamed user with email email@example.com|
|Date Deposited:||18 May 2016 17:51|
|Last Modified:||31 Jul 2016 01:24|