کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
716373 | 892221 | 2012 | 6 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Hierarchical Bayesian ARX models for robust inference
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موضوعات مرتبط
مهندسی و علوم پایه
سایر رشته های مهندسی
مکانیک محاسباتی
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
Gaussian innovations are the typical choice in most ARX models but using other distributions such as the Student's t could be useful. We demonstrate that this choice of distribution for the innovations provides an increased robustness to data anomalies, such as outliers and missing observations. We consider these models in a Bayesian setting and perform inference using numerical procedures based on Markov Chain Monte Carlo methods. These models include automatic order determination by two alternative methods, based on a parametric model order and a sparseness prior, respectively. The methods and the advantage of our choice of innovations are illustrated in three numerical studies using both simulated data and real EEG data.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: IFAC Proceedings Volumes - Volume 45, Issue 16, July 2012, Pages 131-136
Journal: IFAC Proceedings Volumes - Volume 45, Issue 16, July 2012, Pages 131-136