| Автор | DANIEL, THORBURN |
| Дата выпуска | 1986 |
| dc.description | Assume a priori that the log density is a sample function from a Gaussian process subject to the condition that the density integrates to one. The posterior distribution given a number of observations is then still a Gaussian process with the same condition and the same covariance function. The mean value function is changed according to a simple formula. This prior may thus be regarded as a conjugate prior for an unknown density. The mode of the posterior distribution is given implicitly by a simple formula, which can be solved numerically. The mode is a close approximation to the optimal estimate with squared error loss in the discrete case. Some examples with data are given. |
| Формат | application.pdf |
| Издатель | Oxford University Press |
| Копирайт | / 1986 Biometrika Trust |
| Тема | Conjugate prior |
| Тема | Gaussian process |
| Тема | Multinomial probability |
| Тема | Articles |
| Название | A Bayesian approach to density estimation |
| Тип | research-article |
| Electronic ISSN | 1464-3510 |
| Print ISSN | 0006-3444 |
| Журнал | Biometrika |
| Том | 73 |
| Первая страница | 65 |
| Последняя страница | 75 |
| Аффилиация | Department of Statistics, University of StockholmS-1138i5 Stockholm, Sweden |
| Выпуск | 1 |
