التفاصيل البيبلوغرافية
| العنوان: |
Efficient Bayesian Parameter Estimation in Large Discrete Domains |
| المؤلفون: |
Nir Friedman, Yoram Singer |
| المساهمون: |
The Pennsylvania State University CiteSeerX Archives |
| المصدر: |
http://robotics.stanford.edu/~nir/Papers/FS1.ps.gzTest. |
| بيانات النشر: |
MIT Press |
| سنة النشر: |
1999 |
| المجموعة: |
CiteSeerX |
| مصطلحات موضوعية: |
Category, Algorithms and Architectures Presentation preference, none |
| الوصف: |
In this paper we examine the problem of estimating the parameters of a multinomial distribution over a large number of discrete outcomes, most of which do not appear in the training data. We analyze this problem from a Bayesian perspective and develop a hierarchical prior that incorporates the assumption that the observed outcomes constitute only a small subset of the possible outcomes. We show how to efficiently perform exact inference with this form of hierarchical prior and compare our method to standard approaches and demonstrate its merits. Category: Algorithms and Architectures Presentation preference: none This paper was not submitted elsewhere nor will be submitted during NIPS review period. 1 Introduction One of the most important problems in statistical inference is multinomialestimation: Given a past history of observations independent trials with a discrete set of outcomes, predict the probability of the next trial. Such estimators are the basic building blocks in mor. |
| نوع الوثيقة: |
text |
| وصف الملف: |
application/postscript |
| اللغة: |
English |
| العلاقة: |
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.27.4689Test; http://robotics.stanford.edu/~nir/Papers/FS1.ps.gzTest |
| الإتاحة: |
http://robotics.stanford.edu/~nir/Papers/FS1.ps.gzTest |
| حقوق: |
Metadata may be used without restrictions as long as the oai identifier remains attached to it. |
| رقم الانضمام: |
edsbas.9EFF2B28 |
| قاعدة البيانات: |
BASE |
