کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4947633 | 1439589 | 2017 | 14 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Bayesian inference for time-varying applications: Particle-based Gaussian process approaches
ترجمه فارسی عنوان
استنتاج بیزی برای برنامه های مختلف زمان: رویکردهای گاوس بر مبنای ذرات
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
Gaussian process (GP) is a popular non-parametric model for Bayesian inference. However, the performance of GP is often limited in temporal applications, where the input-output pairs are sequentially-ordered, and often exhibit time-varying non-stationarity and heteroscedasticity. In this work, we propose two particle-based GP approaches to capture these distinct temporal characteristics. Firstly, we make use of GP to design two novel state space models which take the temporal order of input-output pairs into account. Secondly, we develop two sequential-Monte-Carlo-inspired particle mechanisms to learn the latent function values and model parameters in a recursive Bayesian framework. Since the model parameters are time-varying, our approaches can model non-stationarity and heteroscedasticity of temporal data. Finally, we evaluate our proposed approaches on a number of challenging time-varying data sets to show effectiveness. By comparing with several related GP approaches, we show that our particle-based GP approaches can efficiently and accurately capture temporal characteristics in time-varying applications.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 238, 17 May 2017, Pages 351-364
Journal: Neurocomputing - Volume 238, 17 May 2017, Pages 351-364
نویسندگان
Yali Wang, Brahim Chaib-draa,