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286
Loopy belief propagation for approximate inference: An empirical study. In:
 Proceedings of Uncertainty in AI,
, 1999
"... Abstract Recently, researchers have demonstrated that "loopy belief propagation" the use of Pearl's polytree algorithm in a Bayesian network with loops can perform well in the context of errorcorrecting codes. The most dramatic instance of this is the near Shannonlimit performanc ..."
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Cited by 676 (15 self)
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; state of the root nodes to 0.9, and we utilized the noisyOR model for the other nodes with a small (0.1) inhibition probability (apart from the leak term, which we inhibited with probability 0.9). This param eterization has the effect of propagating 1 's from the top layer to the bottom. Thus
An Automated CodeBased FaultTree Mitigation Technique
, 1995
"... This paper presents a framework for an automated safety methodology that: (1) generates faulttrees from code, and (2) then applies a faultinjection based technique to mitigate the potential for nonroot nodes to cause hazardous outputs. This methodology reads in source code and userdefined hazard ..."
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This paper presents a framework for an automated safety methodology that: (1) generates faulttrees from code, and (2) then applies a faultinjection based technique to mitigate the potential for nonroot nodes to cause hazardous outputs. This methodology reads in source code and user
Keyword searching and browsing in databases using BANKS
 In ICDE
, 2002
"... With the growth of the Web, there has been a rapid increase in the number of users who need to access online databases without having a detailed knowledge of the schema or of query languages; even relatively simple query languages designed for nonexperts are too complicated for them. We describe BA ..."
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Cited by 321 (14 self)
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keywords, following hyperlinks, and interacting with controls on the displayed results. BANKS models tuples as nodes in a graph, connected by links induced by foreign key and other relationships. Answers to a query are modeled as rooted trees connecting tuples that match individual keywords in the query
Macroscopic models of clique tree growth for Bayesian networks
 In Proceedings of the TwentySecond National Conference on Artificial Intelligence (AAAI07
, 2007
"... In clique tree clustering, inference consists of propagation in a clique tree compiled from a Bayesian network. In this paper, we develop an analytical approach to characterizing clique tree growth as a function of increasing Bayesian network connectedness, speci cally: (i) the expected number of mo ..."
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Cited by 5 (5 self)
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of moral edges in their moral graphs or (ii) the ratio of the number of nonroot nodes to the number of root nodes. In experiments, we systematically increase the connectivity of bipartite Bayesian networks, and nd that clique tree size growth is wellapproximated by Gompertz growth curves. This research
1Acquisition of Causal Models for Local Distributions in Bayesian Networks
"... Abstract—To specify a Bayesian network, a local distribution in the form of a conditional probability table, often of an effect conditioned on itsn causes, needs to be acquired, one for each nonroot node. Since the number of parameters to be assessed is generally exponential in n, improving the eff ..."
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Cited by 1 (0 self)
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Abstract—To specify a Bayesian network, a local distribution in the form of a conditional probability table, often of an effect conditioned on itsn causes, needs to be acquired, one for each nonroot node. Since the number of parameters to be assessed is generally exponential in n, improving
Controlled Generation of Hard and Easy Bayesian Networks: Impact on Maximal Clique Tree in Tree Clustering
 Artificial Intelligence
, 2006
"... This article presents and analyzes algorithms that systematically generate random Bayesian networks of varying difficulty levels, with respect to inference using tree clustering. The results are relevant to research on efficient Bayesian network inference, such as computing a most probable explanati ..."
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Cited by 9 (8 self)
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difficulty level. Our generation algorithms, called BPART and MPART, support controlled but random construction of bipartite and multipartite Bayesian networks. The Bayesian network parameters that we vary are the total number of nodes, degree of connectivity, the ratio of the number of nonroot nodes
Minimum Energy Broadcast on Rectangular Grid Wireless Networks
"... The minimum energy broadcast problem is to assign a transmission range to each node in an ad hoc wireless network to construct a spanning tree rooted at a given source node such that any nonroot node resides within the transmission range of its parent. The objective is to minimize the total energy ..."
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The minimum energy broadcast problem is to assign a transmission range to each node in an ad hoc wireless network to construct a spanning tree rooted at a given source node such that any nonroot node resides within the transmission range of its parent. The objective is to minimize the total energy
Chapter 14 Labeling Schemes
"... Imagine you want to repeatedly query a huge graph, e.g., a social or a road network. For example, you might need to find out whether two nodes are connected, or what the distance between two nodes is. Since the graph is so large, you distribute it among multiple servers in your data center. 14.1 Adj ..."
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.1 Adjacency Theorem 14.1. It is possible to assign labels of size 2 log n bits to nodes in a tree so that for every pair u, v of nodes, it is easy to tell whether u is adjacent to v by just looking at u and v’s labels. Proof. Choose a root in the tree arbitrarily so that every nonroot node has a parent
Chapter 14 Labeling Schemes
"... Imagine you want to repeatedly query a huge graph, e.g., a social or a road network. For example, you might need to find out whether two nodes are connected, or what the distance between two nodes is. Since the graph is so large, you distribute it among multiple servers in your data center. 14.1 Adj ..."
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.1 Adjacency Theorem 14.1. It is possible to assign labels of size 2 log n bits to nodes in a tree so that for every pair u, v of nodes, it is easy to tell whether u is adjacent to v by just looking at u and v’s labels. Proof. Choose a root in the tree arbitrarily so that every nonroot node has a parent
Understanding the Scalability of Bayesian Network Inference using Clique Tree Growth Curves
"... Bayesian networks (BNs) are used to represent and ef ciently compute with multivariate probability distributions in a wide range of disciplines. One of the main approaches to perform computation in BNs is clique tree clustering and propagation. In this approach, BN computation consists of propagati ..."
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Cited by 4 (4 self)
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clique tree growth as a function of parameters that can be computed in polynomial time from BNs, speci cally: (i) the ratio of the number of a BN's nonroot nodes to the number of root nodes, or (ii) the expected number of moral edges in their moral graphs. Our approach is based on combining
Results 1  10
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286