A Tighter Bound for Graphical Models
نویسندگان
چکیده
منابع مشابه
A Tighter Bound for Graphical Models
We present a method to bound the partition function of a Boltzmann machine neural network with any odd-order polynomial. This is a direct extension of the mean-field bound, which is first order. We show that the third-order bound is strictly better than mean field. Additionally, we derive a third-order bound for the likelihood of sigmoid belief networks. Numerical experiments indicate that an e...
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We present a method to bound the partition function of a Boltz-mann machine neural network with any odd order polynomial. This is a direct extension of the mean eld bound, which is rst order. We show that the third order bound is strictly better than mean eld. Additionally we derive a third order bound for the likelihood of sigmoid belief networks. Numerical experiments indicate that an error r...
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هدف اصلی از این تحقیق به دست آوردن و مقایسه حق بیمه باورمندی در مدل های شمارشی گزارش نشده برای داده های طولی می باشد. در این تحقیق حق بیمه های پبش گویی بر اساس توابع ضرر مربع خطا و نمایی محاسبه شده و با هم مقایسه می شود. تمایل به گرفتن پاداش و جایزه یکی از دلایل مهم برای گزارش ندادن تصادفات می باشد و افراد برای استفاده از تخفیف اغلب از گزارش تصادفات با هزینه پائین خودداری می کنند، در این تحقیق ...
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In this paper, we present an O(n1ogn) algorithm to compute a tighter lower bound for the one-dimensional bin packing problem. We have simulated the algorithm on randomly generated bin packing problems with item sizes drawn uniformly from (a, b], where 0 5 a < b 5 B and B is bin size. Using our lower bound, the average error of BFD is less than 2%. For a + b > B, the error is less than 0.003%. K...
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ژورنال
عنوان ژورنال: Neural Computation
سال: 2001
ISSN: 0899-7667,1530-888X
DOI: 10.1162/089976601750399344