التفاصيل البيبلوغرافية
| العنوان: |
Constrained Adjusted Maximum a Posteriori Estimation of Bayesian Network Parameters |
| المؤلفون: |
Ruohai Di, Peng Wang, Chuchao He, Zhigao Guo |
| المصدر: |
Entropy; Volume 23; Issue 10; Pages: 1283 |
| بيانات النشر: |
Multidisciplinary Digital Publishing Institute |
| سنة النشر: |
2021 |
| المجموعة: |
MDPI Open Access Publishing |
| مصطلحات موضوعية: |
graphical models, domain knowledge, prior distribution, equivalent sample size, parameter constraints |
| الوصف: |
Maximum a posteriori estimation (MAP) with Dirichlet prior has been shown to be effective in improving the parameter learning of Bayesian networks when the available data are insufficient. Given no extra domain knowledge, uniform prior is often considered for regularization. However, when the underlying parameter distribution is non-uniform or skewed, uniform prior does not work well, and a more informative prior is required. In reality, unless the domain experts are extremely unfamiliar with the network, they would be able to provide some reliable knowledge on the studied network. With that knowledge, we can automatically refine informative priors and select reasonable equivalent sample size (ESS). In this paper, considering the parameter constraints that are transformed from the domain knowledge, we propose a Constrained adjusted Maximum a Posteriori (CaMAP) estimation method, which is featured by two novel techniques. First, to draw an informative prior distribution (or prior shape), we present a novel sampling method that can construct the prior distribution from the constraints. Then, to find the optimal ESS (or prior strength), we derive constraints on the ESS from the parameter constraints and select the optimal ESS by cross-validation. Numerical experiments show that the proposed method is superior to other learning algorithms. |
| نوع الوثيقة: |
text |
| وصف الملف: |
application/pdf |
| اللغة: |
English |
| العلاقة: |
Information Theory, Probability and Statistics; https://dx.doi.org/10.3390/e23101283Test |
| DOI: |
10.3390/e23101283 |
| الإتاحة: |
https://doi.org/10.3390/e23101283Test |
| حقوق: |
https://creativecommons.org/licenses/by/4.0Test/ |
| رقم الانضمام: |
edsbas.DD9DCF58 |
| قاعدة البيانات: |
BASE |
