کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
10524892 | 957763 | 2012 | 7 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
On Bayesian learning via loss functions
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
ریاضیات
ریاضیات کاربردی
پیش نمایش صفحه اول مقاله
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چکیده انگلیسی
We provide a decision theoretic approach to the construction of a learning process in the presence of independent and identically distributed observations. Starting with a probability measure representing beliefs about a key parameter, the approach allows the measure to be updated via the solution to a well defined decision problem. While the learning process encompasses the Bayesian approach, a necessary asymptotic consideration then actually implies the Bayesian learning process is best. This conclusion is due to the requirement of posterior consistency for all models and of having standardized losses between probability distributions. This is shown considering a specific continuous model and a very general class of discrete models.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Statistical Planning and Inference - Volume 142, Issue 12, December 2012, Pages 3167-3173
Journal: Journal of Statistical Planning and Inference - Volume 142, Issue 12, December 2012, Pages 3167-3173
نویسندگان
Pier Giovanni Bissiri, Stephen G. Walker,