دانلود مقاله ISI انگلیسی شماره 29029
عنوان فارسی مقاله

درک مقیاس پذیری استنباط شبکه بیزی با استفاده از منحنی های رشد درخت دسته

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
29029 2010 23 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Understanding the scalability of Bayesian network inference using clique tree growth curves
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Artificial Intelligence, Volume 174, Issues 12–13, August 2010, Pages 984–1006

کلمات کلیدی
/ استدلال احتمالاتی - شبکه های بیزی - خوشه درخت دسته - رشد درخت دسته - تقریب به طور مداوم - منحنی های رشد گومپرتز - آزمایش های کنترل شده - رگرسیون -
پیش نمایش مقاله
پیش نمایش مقاله درک مقیاس پذیری استنباط شبکه بیزی با استفاده از منحنی های رشد درخت دسته

چکیده انگلیسی

One of the main approaches to performing computation in Bayesian networks (BNs) is clique tree clustering and propagation. The clique tree approach consists of propagation in a clique tree compiled from a BN, and while it was introduced in the 1980s, there is still a lack of understanding of how clique tree computation time depends on variations in BN size and structure. In this article, we improve this understanding by developing an approach to characterizing clique tree growth as a function of parameters that can be computed in polynomial time from BNs, specifically: (i) the ratio of the number of a BN's non-root nodes to the number of root nodes, and (ii) the expected number of moral edges in their moral graphs. Analytically, we partition the set of cliques in a clique tree into different sets, and introduce a growth curve for the total size of each set. For the special case of bipartite BNs, there are two sets and two growth curves, a mixed clique growth curve and a root clique growth curve. In experiments, where random bipartite BNs generated using the BPART algorithm are studied, we systematically increase the out-degree of the root nodes in bipartite Bayesian networks, by increasing the number of leaf nodes. Surprisingly, root clique growth is well-approximated by Gompertz growth curves, an S-shaped family of curves that has previously been used to describe growth processes in biology, medicine, and neuroscience. We believe that this research improves the understanding of the scaling behavior of clique tree clustering for a certain class of Bayesian networks; presents an aid for trade-off studies of clique tree clustering using growth curves; and ultimately provides a foundation for benchmarking and developing improved BN inference and machine learning algorithms.

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