Slice Sampling Normalized Kernel-Weighted Completely Random Measure Mixture Models
Abstract
A number of dependent nonparametric processes have been proposed to model non-stationary data with unknown latent dimensionality. However, the inference algorithms are often slow and unwieldy, and are in general highly specific to a given model formulation. In this paper, we describe a wide class of nonparametric processes, including several existing models, and present a slice sampler that allows efficient inference across this class of models.
Cite
Text
Foti and Williamson. "Slice Sampling Normalized Kernel-Weighted Completely Random Measure Mixture Models." Neural Information Processing Systems, 2012.Markdown
[Foti and Williamson. "Slice Sampling Normalized Kernel-Weighted Completely Random Measure Mixture Models." Neural Information Processing Systems, 2012.](https://mlanthology.org/neurips/2012/foti2012neurips-slice/)BibTeX
@inproceedings{foti2012neurips-slice,
title = {{Slice Sampling Normalized Kernel-Weighted Completely Random Measure Mixture Models}},
author = {Foti, Nicholas and Williamson, Sinead},
booktitle = {Neural Information Processing Systems},
year = {2012},
pages = {2240-2248},
url = {https://mlanthology.org/neurips/2012/foti2012neurips-slice/}
}