Stochastic Algorithms for Searching Causal Orderings in Bayesian Networks

The quantitative evaluation of producer and injector well interference based on well operation data (profiles of flow rates/injectivities and bottomhole/reservoir pressures) with the help of CRM (Capacitance-Resistive Models) is an optimization problem with large set of variables and constraints. The analytical solution cannot be found because of the complex form of the objective function for this problem. Attempts to find the solution with stochastic algorithms take unacceptable time and the result may be far from the optimal solution. Besides, the use of universal (commercial) optimizers hides the details of step by step solution from the user, for example — the ambiguity of the solution as the result of data inaccuracy.<br> The present article concerns two variants of CRM problem. The authors present a new algorithm of solving the problems with the help of “General Quadratic Programming Algorithm”. The main advantage of the new algorithm is the greater performance in comparison with the other known algorithms. Its other advantage is the possibility of an ambiguity analysis. This article studies the conditions which guarantee that the first variant of problem has a unique solution, which can be found with the presented algorithm. Another algorithm for finding the approximate solution for the second variant of the problem is also considered. The method of visualization of approximate solutions set is presented. The results of experiments comparing the new algorithm with some previously known are given.

Download Full-text

The impact of subway operation on urban traffic: A GRA-BNs based study

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189992 ◽

2021 ◽

pp. 1-16

Author(s):

Lixin Yan ◽

Tao Zeng ◽

Yubing Xiong ◽

Zhenyun Li ◽

Qingmei Liu

Keyword(s):

Bayesian Networks ◽

Operating Characteristic ◽

Urban Traffic ◽

Relational Analysis ◽

Traffic Security ◽

Structural Network ◽

Random Decision Forest ◽

Roc Area ◽

Grey Relational ◽

The Impact

With the development of urbanization, urban traffic has exposed many problems. To study the subway’s influence on urban traffic, this paper collects data on traffic indicators in Nanchang from 2008 to 2018. The research is carried out from three aspects: traffic accessibility, green traffic, and traffic security. First, Grey Relational Analysis is used to select 18 traffic indicators correlated with the subway from 22 traffic indicators. Second, the data is discretized and learned based on Bayesian Networks to construct the structural network of the subway’s influence. Third, to verify the reliability of using GRA and the effectiveness of Bayesian Networks (GRA-BNs), Bayesian Networks with full indicators analysis and other four algorithms (Naive Bayes, Random Decision Forest, Logistic and regression) are employed for comparison. Moreover, the receiver operating characteristic (ROC) area, true positive (TP) rate, false positive (FP) rate, precision, recall, F-measure, and accuracy are utilized for comparing each situation. The result shows that GRA-BNs is the most effective model to study the impact of the subway’s operation on urban traffic. Then, the dependence relations between the subway and each index are analyzed by the conditional probability tables (CPTs). Finally, according to the analysis, some suggestions are put forward.

Download Full-text

Workload-aware Materialization for Efficient Variable Elimination on Bayesian Networks

2021 IEEE 37th International Conference on Data Engineering (ICDE) ◽

10.1109/icde51399.2021.00104 ◽

2021 ◽

Author(s):

Cigdem Aslay ◽

Martino Ciaperoni ◽

Aristides Gionis ◽

Michael Mathioudakis

Keyword(s):

Bayesian Networks ◽

Variable Elimination

Download Full-text