Optional P\'{o}lya tree and Bayesian inference

By Wing Hung Wong, Li Ma. 2010

51 citations recorded by the source corpus.

We introduce an extension of the Polya tree approach for constructing distributions on the space of probability measures. By using optional stopping and optional choice of splitting variables, the construction gives rise to random measures that are absolutely continuous with piecewise smooth densities on partitions that can adapt to fit the data. The resulting "optional Polya tree" distribution has large support in total variation topology and yields posterior distributions that are also optional Polya trees with computable parameter values.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/e66435fed8d6fde135ed770510330c26120f694e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Hui Jiang
2. Wing Hung Wong
3. Luo Lu
4. Heng Li
5. Nils Homer

Citations in the corpus

• Multivariate Density Estimation by Bayesian Sequential Partitioning (2013, background): “Very recently Wong and Ma (2010) discussed the problem of Bayesian inference of the partition.”