Structural Learning of Bayesian Networks Via Constrained Hill Climbing Algorithms: Adjusting Trade-off between Efficiency and Accuracy

2014 ◽  
Vol 30 (3) ◽  
pp. 292-325 ◽  
Author(s):  
Jacinto Arias ◽  
José A. Gámez ◽  
José M. Puerta
Keyword(s):  
Bayesian Networks ◽  
Hill Climbing ◽  
Structural Learning ◽  
Trade Off ◽  
Hill Climbing Algorithms

Structural learning of Bayesian networks using local algorithms based on the space of orderings

2010 ◽  
Vol 15 (10) ◽  
pp. 1881-1895 ◽  
Author(s):  
Juan I. Alonso-Barba ◽  
Luis delaOssa ◽  
Jose M. Puerta
Keyword(s):  
Bayesian Networks ◽  
Structural Learning ◽  
Local Algorithms

scholarly journals Gaussian Mixture Reduction for Time-Constrained Approximate Inference in Hybrid Bayesian Networks

2019 ◽  
Vol 9 (10) ◽  
pp. 2055 ◽  
Author(s):  
Cheol Young Park ◽  
Kathryn Blackmond Laskey ◽  
Paulo C. G. Costa ◽  
Shou Matsumoto
Keyword(s):  
Bayesian Networks ◽  
Message Passing ◽  
Random Variables ◽  
Computation Time ◽  
Gaussian Mixture ◽  
Image Understanding ◽  
Approximate Inference ◽  
Continuous Variables ◽  
Hybrid Bayesian Networks ◽  
Trade Off

Hybrid Bayesian Networks (HBNs), which contain both discrete and continuous variables, arise naturally in many application areas (e.g., image understanding, data fusion, medical diagnosis, fraud detection). This paper concerns inference in an important subclass of HBNs, the conditional Gaussian (CG) networks, in which all continuous random variables have Gaussian distributions and all children of continuous random variables must be continuous. Inference in CG networks can be NP-hard even for special-case structures, such as poly-trees, where inference in discrete Bayesian networks can be performed in polynomial time. Therefore, approximate inference is required. In approximate inference, it is often necessary to trade off accuracy against solution time. This paper presents an extension to the Hybrid Message Passing inference algorithm for general CG networks and an algorithm for optimizing its accuracy given a bound on computation time. The extended algorithm uses Gaussian mixture reduction to prevent an exponential increase in the number of Gaussian mixture components. The trade-off algorithm performs pre-processing to find optimal run-time settings for the extended algorithm. Experimental results for four CG networks compare performance of the extended algorithm with existing algorithms and show the optimal settings for these CG networks.

A Bayesian network structural learning algorithm for calculating the failure probabilities of complex engineering systems with limited data

2021 ◽  
pp. 1-14
Author(s):  
Yong Chen ◽  
Tianbao Zhang ◽  
Ruojun Wang ◽  
Lei Cai
Keyword(s):  
Bayesian Network ◽  
Learning Algorithm ◽  
Hill Climbing ◽  
Structural Learning ◽  
Engineering Systems ◽  
Limited Data ◽  
Model Complex ◽  
Complex Engineering ◽  
Complex Engineering Systems ◽  
Three Components

The failure of complex engineering systems is easy to lead to disastrous consequences. To prevent the failure, it is necessary to model complex engineering systems using probabilistic techniques with limited data which is a major feature of complex engineering systems. It is a good choice to perform such modeling using Bayesian network because of its advantages in probabilistic modeling. However, few Bayesian network structural learning algorithms are designed for complex engineering systems with limited data. Therefore, an algorithm for learning the Bayesian network structure of them should be developed. Based on the process of self-purification of water, a complex engineering system is segmented into three components according to the degree of difficulty in solving them. And then a Bayesian network learning algorithm with three components (TC), including PC algorithm, MIK algorithm which is originated by the paper through combining Mutual Information and K2 algorithm, and the Hill-Climbing method, is developed, i.e. TC algorithm. To verify its effectiveness, TC algorithm, K2 algorithm, and Max-Min Hill-Climbing are respectively used to learn Alarm network with different sizes of samples. The results imply that TC algorithm has the best performance. Finally, TC algorithm is applied to study tank spill accidents with 220 samples.

scholarly journals Structural learning of Bayesian networks by bacterial foraging optimization

2016 ◽  
Vol 69 ◽  
pp. 147-167 ◽  
Author(s):  
Cuicui Yang ◽  
Junzhong Ji ◽  
Jiming Liu ◽  
Jinduo Liu ◽  
Baocai Yin
Keyword(s):  
Bayesian Networks ◽  
Structural Learning ◽  
Bacterial Foraging Optimization ◽  
Bacterial Foraging

Crossing fitness valleys during the evolution of limpet homing behaviour

2010 ◽  
Vol 5 (2) ◽  
pp. 274-282 ◽  
Author(s):  
Richard Stafford
Keyword(s):  
Path Integration ◽  
Fitness Landscapes ◽  
Hill Climbing ◽  
Global Maximum ◽  
Trail Following ◽  
Stepping Stones ◽  
Temporary Reduction ◽  
Homing Behaviour ◽  
Fitness Value ◽  
Hill Climbing Algorithms

AbstractEvolution is often considered a gradual hill-climbing process, slowly increasing the fitness of organisms. Here I investigate evolution of homing behaviour in simulated intertidal limpets. While the simulation of homing is only a possible mechanism by which homing may have evolved, the process allows an investigation of how evolution may occur over different fitness landscapes. With some fitness landscapes, in order to evolve path integration as a homing mechanism, a temporary reduction in an organism’s fitness was required — since high developmental costs occurred before successful homing strategies evolved. Simple hill-climbing algorithms, therefore, only rarely resulted in the evolution of a functional homing behaviour. The inclusion of trail-following greatly increases the frequency of success of evolution of a path integration strategy. Initially an emergent homing behaviour is formed combining path integration with trail-following. This also demonstrates evolution through exaptation, since in the simulation, the original role of trail-following is likely to be unrelated to homing. Analysis of the fitness landscapes of homing in the presence of trail-following behaviour shows a high variability of fitness, which results in the formation of ‘stepping-stones’ of high fitness across fitness valleys. By using these stepping-stones, simple hill-climbing algorithms can reach the global maximum fitness value.

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