Griffin, J;
Leisen, F;
(2018)
Modelling and Computation Using NCoRM Mixtures for Density Regression.
Bayesian Analysis
, 13
(3)
pp. 897-916.
10.1214/17-BA1072.
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Abstract
Normalized compound random measures are flexible nonparametric priors for related distributions. We consider building general nonparametric regression models using normalized compound random measure mixture models. Posterior inference is made using a novel pseudo-marginal Metropolis-Hastings sampler for normalized compound random measure mixture models. The algorithm makes use of a new general approach to the unbiased estimation of Laplace functionals of compound random measures (which includes completely random measures as a special case). The approach is illustrated on problems of density regression.
| Type: | Article |
|---|---|
| Title: | Modelling and Computation Using NCoRM Mixtures for Density Regression |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1214/17-BA1072 |
| Publisher version: | http://dx.doi.org/10.1214/17-BA1072 |
| Language: | English |
| Additional information: | © 2018 International Society for Bayesian Analysis. This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/). |
| Keywords: | dependent random measures, mixture models, multivariate Lévy measures, pseudo-marginal samplers, Poisson estimator |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10068421 |
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