| Autor: |
Nguyen, T Tin, Nguyen, Hien D, Chamroukhi, Faicel, McLachlan, Geoffrey J |
| Rok vydání: |
2019 |
| Předmět: |
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| Zdroj: |
Cogent Mathematics & Statistics. 7:1 (2020) |
| Druh dokumentu: |
Working Paper |
| DOI: |
10.1080/25742558.2020.1750861 |
| Popis: |
Given sufficiently many components, it is often cited that finite mixture models can approximate any other probability density function (pdf) to an arbitrary degree of accuracy. Unfortunately, the nature of this approximation result is often left unclear. We prove that finite mixture models constructed from pdfs in $\mathcal{C}_{0}$ can be used to conduct approximation of various classes of approximands in a number of different modes. That is, we prove approximands in $\mathcal{C}_{0}$ can be uniformly approximated, approximands in $\mathcal{C}_{b}$ can be uniformly approximated on compact sets, and approximands in $\mathcal{L}_{p}$ can be approximated with respect to the $\mathcal{L}_{p}$, for $p\in\left[1,\infty\right)$. Furthermore, we also prove that measurable functions can be approximated, almost everywhere. |
| Databáze: |
arXiv |
| Externí odkaz: |
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