کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
976245 | 933099 | 2010 | 9 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Parameter inference in small world network disease models with approximate Bayesian Computational methods
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
ریاضیات
فیزیک ریاضی
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
Small world network models have been effective in capturing the variable behaviour of reported case data of the SARS coronavirus outbreak in Hong Kong during 2003. Simulations of these models have previously been realized using informed “guesses” of the proposed model parameters and tested for consistency with the reported data by surrogate analysis. In this paper we attempt to provide statistically rigorous parameter distributions using Approximate Bayesian Computation sampling methods. We find that such sampling schemes are a useful framework for fitting parameters of stochastic small world network models where simulation of the system is straightforward but expressing a likelihood is cumbersome.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Physica A: Statistical Mechanics and its Applications - Volume 389, Issue 3, 1 February 2010, Pages 540-548
Journal: Physica A: Statistical Mechanics and its Applications - Volume 389, Issue 3, 1 February 2010, Pages 540-548
نویسندگان
David M. Walker, David Allingham, Heung Wing Joseph Lee, Michael Small,