Relevant bibliographies by topics / Junction tree algorithm

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Academic literature on the topic 'Junction tree algorithm'

Author: Grafiati
Published: 4 June 2021
Last updated: 14 February 2022

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Journal articles on the topic "Junction tree algorithm"

1

Gildea, Daniel. "Grammar Factorization by Tree Decomposition." Computational Linguistics 37, no. 1 (2011): 231–48. http://dx.doi.org/10.1162/coli_a_00040.

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We describe the application of the graph-theoretic property known as treewidth to the problem of finding efficient parsing algorithms. This method, similar to the junction tree algorithm used in graphical models for machine learning, allows automatic discovery of efficient algorithms such as the O(n4) algorithm for bilexical grammars of Eisner and Satta. We examine the complexity of applying this method to parsing algorithms for general Linear Context-Free Rewriting Systems. We show that any polynomial-time algorithm for this problem would imply an improved approximation algorithm for the well-studied treewidth problem on general graphs.
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2

Wu, Peijie, Xianghai Meng, Li Song, and Wenze Zuo. "Crash Risk Evaluation and Crash Severity Pattern Analysis for Different Types of Urban Junctions: Fault Tree Analysis and Association Rules Approaches." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 1 (2019): 403–16. http://dx.doi.org/10.1177/0361198118822817.

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Urban junctions usually present significant safety concerns, and the majority of all crashes in urban areas occur in or near junctions. Factors contributing to crash severity at junctions have been explored, but crash risk levels and crash severity patterns of different junction types have hardly been investigated. In order to fill this gap, this study analyzed the safety performance of six junction types and the factors contributing to crash severity, in order to assist city transportation authorities to implement effective countermeasures. Fault tree analysis (FTA) was applied for the risk evaluation of urban junctions and association rules (AR) algorithm was employed for the crash severity pattern analysis based on data from the U.K. STATS19 database from 2012 to 2016. Overall, four types of urban junctions with high crash risk level and over 4,000 AR contributing to crash severity are identified in the present paper. The results show that: (a) roundabouts and mini-roundabouts have the lowest fatality and casualty rates while T-junctions or staggered junctions and crossroads have the highest crash risk levels; (b) FTA may produce inaccurate outcomes because of incorrect logic gates, but AR can generate real potential relationships between crash severity and risk factors; (c) crash severity patterns are quite complex and the interdependence between risk factors is different for each junction type; (d) risk factors such as male driver, no physical crossing facilities within 50 meters, and give way or uncontrolled junction are common in high-risk junctions at night.
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3

Madsen, Anders L., and Finn V. Jensen. "Lazy propagation: A junction tree inference algorithm based on lazy evaluation." Artificial Intelligence 113, no. 1-2 (1999): 203–45. http://dx.doi.org/10.1016/s0004-3702(99)00062-4.

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4

Parmentier, Axel, Victor Cohen, Vincent Leclère, Guillaume Obozinski, and Joseph Salmon. "Integer Programming on the Junction Tree Polytope for Influence Diagrams." INFORMS Journal on Optimization 2, no. 3 (2020): 209–28. http://dx.doi.org/10.1287/ijoo.2019.0036.

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Influence diagrams (ID) and limited memory influence diagrams (LIMID) are flexible tools to represent discrete stochastic optimization problems, with the Markov decision process (MDP) and partially observable MDP as standard examples. More precisely, given random variables considered as vertices of an acyclic digraph, a probabilistic graphical model defines a joint distribution via the conditional distributions of vertices given their parents. In an ID, the random variables are represented by a probabilistic graphical model whose vertices are partitioned into three types: chance, decision, and utility vertices. The user chooses the distribution of the decision vertices conditionally to their parents in order to maximize the expected utility. Leveraging the notion of rooted junction tree, we present a mixed integer linear formulation for solving an ID, as well as valid inequalities, which lead to a computationally efficient algorithm. We also show that the linear relaxation yields an optimal integer solution for instances that can be solved by the “single policy update,” the default algorithm for addressing IDs.
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5

SIRINANDA, K. G., M. BRAZIL, P. A. GROSSMAN, J. H. RUBINSTEIN, and D. A. THOMAS. "OPTIMAL LOCATION OF AN UNDERGROUND CONNECTOR USING DISCOUNTED STEINER TREE THEORY." ANZIAM Journal 62, no. 3 (2020): 334–51. http://dx.doi.org/10.1017/s1446181120000231.

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AbstractThe objective of this paper is to demonstrate that the gradient-constrained discounted Steiner point algorithm (GCDSPA) described in an earlier paper by the authors is applicable to a class of real mine planning problems, by using the algorithm to design a part of the underground access in the Rubicon gold mine near Kalgoorlie in Western Australia. The algorithm is used to design a decline connecting two ore bodies so as to maximize the net present value (NPV) associated with the connector. The connector is to break out from the access infrastructure of one ore body and extend to the other ore body. There is a junction on the connector where it splits in two near the second ore body. The GCDSPA is used to obtain the optimal location of the junction and the corresponding NPV. The result demonstrates that the GCDSPA can be used to solve certain problems in mine planning for which currently available methods cannot provide optimal solutions.
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6

Szántai, Tamás, and Edith Kovács. "Discovering a junction tree behind a Markov network by a greedy algorithm." Optimization and Engineering 14, no. 4 (2013): 503–18. http://dx.doi.org/10.1007/s11081-013-9232-8.

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7

Zhao, Yi, Jianxiao Ma, Linghong Shen, and Yong Qian. "Optimizing the Junction-Tree-Based Reinforcement Learning Algorithm for Network-Wide Signal Coordination." Journal of Advanced Transportation 2020 (February 21, 2020): 1–11. http://dx.doi.org/10.1155/2020/6489027.

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This study develops three measures to optimize the junction-tree-based reinforcement learning (RL) algorithm, which will be used for network-wide signal coordination. The first measure is to optimize the frequency of running the junction-tree algorithm (JTA) and the intersection status division. The second one is to optimize the JTA information transmission mode. The third one is to optimize the operation of a single intersection. A test network and three test groups are built to analyze the optimization effect. Group 1 is the control group, group 2 adopts the optimizations for the basic parameters and the information transmission mode, and group 3 adopts optimizations for the operation of a single intersection. Environments with different congestion levels are also tested. Results show that optimizations of the basic parameters and the information transmission mode can improve the system efficiency and the flexibility of the green light, and optimizing the operation of a single intersection can improve the efficiency of both the system and the individual intersection. By applying the proposed optimizations to the existing JTA-based RL algorithm, network-wide signal coordination can perform better.
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8

MADSEN, ANDERS L., and BRUCE D'AMBROSIO. "A FACTORIZED REPRESENTATION OF INDEPENDENCE OF CAUSAL INFLUENCE AND LAZY PROPAGATION." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 08, no. 02 (2000): 151–65. http://dx.doi.org/10.1142/s0218488500000113.

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The efficiency of algorithms for probabilistic inference in Bayesian networks can be improved by exploiting independence of causal influence. The factorized representation of independence of causal influence offers a factorized decomposition of certain independence of causal influence models. We describe how LAZY propagation - a junction tree based inference algorithm - easily can be extended to take advantage of the decomposition offered by the factorized representation. We introduce two extensions to the factorized representation easing the knowledge acquisition task and reducing the space complexity of the representation exponentially in the state space size of the effect variable of an independence of causal influence model. We describe how the factorized representation can be used to solve tasks such as calculating the maximum a posteriori hypotheses, the maximum expected utility, and the most probable configuration. Finally, the results of an empirical evaluation indicate that considerable performance improvements can be obtained using LAZY propagation combined with the factorized representation compared to LAZY propagation performed in junction trees constructed after either parent divorcing or temporal transformation have been applied to the Bayesian network.
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9

Wang, Dan, Ying Tian, Tai Yong Wang, Shi Feng Ye, and Qiong Liu. "Fault Diagnosis of the Foundation Brake Rigging System Based on Fault Tree and Bayesian Network." Key Engineering Materials 693 (May 2016): 1734–40. http://dx.doi.org/10.4028/www.scientific.net/kem.693.1734.

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Based on the analysis of the advantages and limits of the traditional fault tree and Bayesian network in fault diagnosis, the method that building the fault Bayesian network based on fault tree is proposed in this paper. The paper introduces the correspondences between elements of the fault tree and the fault Bayesian network, also describes the inference process of the junction tree algorithm in the fault Bayesian network. Then with the foundation brake rigging system of CRH380AL EMU as an example, we build up the fault tree, complete its transmission to the fault Bayesian network, proving the superiority of the fault Bayesian tree in fault analysis of the complex system at last.
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10

Ryu, Hyejeong. "Hierarchical Path-Planning for Mobile Robots Using a Skeletonization-Informed Rapidly Exploring Random Tree*." Applied Sciences 10, no. 21 (2020): 7846. http://dx.doi.org/10.3390/app10217846.

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An efficient, hierarchical, two-dimensional (2D) path-planning method for large complex environments is presented in this paper. For mobile robots moving in 2D environments, conventional path-planning algorithms employ single-layered maps; the proposed approach engages in hierarchical inter- and intra-regional searches. A navigable graph of an environment is constructed using segmented local grid maps and safe junction nodes. An inter-regional path is obtained using the navigable graph and a graph-search algorithm. A skeletonization-informed rapidly exploring random tree* (SIRRT*) efficiently computes converged intra-regional paths for each map segment. The sampling process of the proposed hierarchical path-planning algorithm is locally conducted only in the start and goal regions, whereas the conventional path-planning should process the sampling over the entire environment. The entire path from the start position to the goal position can be achieved more quickly and more robustly using the hierarchical approach than the conventional single-layered method. The performance of the hierarchical path-planning is analyzed using a publicly available benchmark environment.
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