Characterizing variation of nonparametric random probability measures using the Kullback–Leibler divergence
This work characterizes the dispersion of some popular random probability measures, including the bootstrap, the Bayesian bootstrap, and the Pólya tree prior. This dispersion is measured in terms of the variation of the Kullback–Leibler divergence of a random draw from the process to that of its bas...
| Main Authors: | , , |
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| Format: | Journal article |
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Taylor and Francis
2016
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| _version_ | 1797066581025488896 |
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| author | Watson, J Nieto-Barajas, L Holmes, C |
| author_facet | Watson, J Nieto-Barajas, L Holmes, C |
| author_sort | Watson, J |
| collection | OXFORD |
| description | This work characterizes the dispersion of some popular random probability measures, including the bootstrap, the Bayesian bootstrap, and the Pólya tree prior. This dispersion is measured in terms of the variation of the Kullback–Leibler divergence of a random draw from the process to that of its baseline centring measure. By providing a quantitative expression of this dispersion around the baseline distribution, our work provides insight for comparing different parameterizations of the models and for the setting of prior parameters in applied Bayesian settings. This highlights some limitations of the existing canonical choice of parameter settings in the Pólya tree process. |
| first_indexed | 2024-03-06T21:44:07Z |
| format | Journal article |
| id | oxford-uuid:48f47373-28e0-49e8-9729-1251b764bd7a |
| institution | University of Oxford |
| last_indexed | 2024-03-06T21:44:07Z |
| publishDate | 2016 |
| publisher | Taylor and Francis |
| record_format | dspace |
| spelling | oxford-uuid:48f47373-28e0-49e8-9729-1251b764bd7a2022-03-26T15:28:47ZCharacterizing variation of nonparametric random probability measures using the Kullback–Leibler divergenceJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:48f47373-28e0-49e8-9729-1251b764bd7aSymplectic Elements at OxfordTaylor and Francis2016Watson, JNieto-Barajas, LHolmes, CThis work characterizes the dispersion of some popular random probability measures, including the bootstrap, the Bayesian bootstrap, and the Pólya tree prior. This dispersion is measured in terms of the variation of the Kullback–Leibler divergence of a random draw from the process to that of its baseline centring measure. By providing a quantitative expression of this dispersion around the baseline distribution, our work provides insight for comparing different parameterizations of the models and for the setting of prior parameters in applied Bayesian settings. This highlights some limitations of the existing canonical choice of parameter settings in the Pólya tree process. |
| spellingShingle | Watson, J Nieto-Barajas, L Holmes, C Characterizing variation of nonparametric random probability measures using the Kullback–Leibler divergence |
| title | Characterizing variation of nonparametric random probability measures using the Kullback–Leibler divergence |
| title_full | Characterizing variation of nonparametric random probability measures using the Kullback–Leibler divergence |
| title_fullStr | Characterizing variation of nonparametric random probability measures using the Kullback–Leibler divergence |
| title_full_unstemmed | Characterizing variation of nonparametric random probability measures using the Kullback–Leibler divergence |
| title_short | Characterizing variation of nonparametric random probability measures using the Kullback–Leibler divergence |
| title_sort | characterizing variation of nonparametric random probability measures using the kullback leibler divergence |
| work_keys_str_mv | AT watsonj characterizingvariationofnonparametricrandomprobabilitymeasuresusingthekullbackleiblerdivergence AT nietobarajasl characterizingvariationofnonparametricrandomprobabilitymeasuresusingthekullbackleiblerdivergence AT holmesc characterizingvariationofnonparametricrandomprobabilitymeasuresusingthekullbackleiblerdivergence |