کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
417252 681474 2008 20 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Maximum entropy and least square error minimizing procedures for estimating missing conditional probabilities in Bayesian networks
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نظریه محاسباتی و ریاضیات
پیش نمایش صفحه اول مقاله
Maximum entropy and least square error minimizing procedures for estimating missing conditional probabilities in Bayesian networks
چکیده انگلیسی

Conditional probability tables (CPT) in many Bayesian networks often contain missing values. The problem of missing values in CPT is a very common problem and occurs due to the lack of data on certain scenarios that are observed in the real world but are missing in the training data. The current approaches of addressing the problem of missing values in CPT are very restrictive in that they assume certain probability distributions for estimating missing values. Recently, maximum entropy (ME) approaches have been used to learn features of probability distribution functions from the observed data. The ME approaches do not require any data distribution assumptions and are shown to work well for several non-parametric distributions. The ME and least square (LS) error minimizing approaches can be used for estimating missing values in CPT for Bayesian networks. The applications of ME and LS approaches for estimating missing CPT require researchers to solve difficult constrained non-linear optimization problems. These difficult constrained non-linear optimization problems can be solved using genetic algorithms.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computational Statistics & Data Analysis - Volume 52, Issue 7, 15 March 2008, Pages 3583–3602
نویسندگان
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