کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4945297 | 1438423 | 2017 | 19 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Theory and computations for the Dirichlet process and related models: An overview
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Theory and computations for the Dirichlet process and related models: An overview Theory and computations for the Dirichlet process and related models: An overview](/preview/png/4945297.png)
چکیده انگلیسی
Data analysis sometimes requires the relaxation of parametric assumptions in order to gain modeling flexibility and robustness against mis-specification of the probability model. In the Bayesian context, this is accomplished by placing a prior distribution on an infinite-dimensional space, referred to as Bayesian nonparametric models. We provide an overview on the most popular Bayesian nonparametric models for probability distributions and for collections of predictor-dependent probability distributions. The intention of is not to be complete or exhaustive, but rather to touch on areas of interest for the practical use of the priors in the context of a hierarchical model. We give an overview covering the main properties of the basic models and the algorithms for fitting them.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: International Journal of Approximate Reasoning - Volume 81, February 2017, Pages 128-146
Journal: International Journal of Approximate Reasoning - Volume 81, February 2017, Pages 128-146
نویسندگان
Alejandro Jara,