Active inference for dynamic Bayesian networks with an application to tissue engineering

Komurlu, Caner; Shao, Jinjian; Akar, Banu; Bayrak, Elif Seyma; Brey, Eric M.; Çınar, Ali; Bilgic, Mustafa

doi:10.1007/s10115-016-0963-7

“…Approximate inference intends to find a successful solution in the shortest time possible. Its approach uses the DBN factors to estimate the joint distribution and, in many cases, use additional information to support the inference [55]. Carrying out inference on the different time-slices is a challenge, and requires important design considerations, such as whether to include supporting information and which DBN segment should be applied to perform the inference.…”

Section: Rq3: What Advances Have Been Made With Respect To Dbn Inference?mentioning

confidence: 99%

“…Content may change prior to final publication. [55] Error Rate Non-homogenous Inference [57] Error Rate Inference with constraints and sliding window [12] Time Inference with qualitative information [58] Coherence…”

Section: Idmentioning

confidence: 99%

Dynamic Bayesian Network Modeling, Learning, and Inference: A Survey

Shiguihara

¹

,

Lopes

²

,

Mauricio

³

2021

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Since the introduction of Dynamic Bayesian Networks (DBNs), their efficiency and effectiveness have increased through the development of three significant aspects: (i) modeling, (ii) learning and (iii) inference. However, no reviews of the literature have been found that chronicle their importance and development over time. The aim of this study is to provide a systematic review of the literature that details the evolution and advancement of DBNs, focusing in the period 1997-2019 that emphasize the aspects of modeling, learning and inference. While the literature presents temporal event networks, knowledge encapsulation, relational and time varying representations as the four predominant DBN modeling approaches, this work groups them as essential techniques within DBNs and help practitioners by associating each to various challenge that arise in pattern discovery and prediction in dynamic processes. Regarding learning, the predominant methods mainly focus on scoring with greedy search. Finally, our study suggests that the main methods used in DBN inference extend or adapt those used in static BNs, and are oriented to either optimize processing time or error rate.INDEX TERMS Dynamic Bayesian Networks, dynamic probabilistic graphical models, literature review, systematic literature review.

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“…DBNs have applications in various fields, including artificial intelligence, machine learning, and automatic control 2 . Furthermore, DBNs have a broad range of engineering applications, such as managing transcriptional regulatory relationships between cancer genes 3 , identifying connectivity issues between human brain regions through high-order DBNs using functional magnetic resonance imaging time series data 4 , and analyzing the vascularization in the formation process of engineered tissues, aiming to enhance the accuracy of predicting future time steps and ensuring an acceptable uncertainty in forecasting the future progress of the organization 5 . Integrating structural prediction methods, such as mutual information and maximum information coefficient into the DBN model enhances the efficiency and scale of gene regulatory network reconstruction 6 .…”

Section: Introductionmentioning

confidence: 99%

Dynamic Bayesian network structure learning based on an improved bacterial foraging optimization algorithm

Meng,

Cong,

Li

et al. 2024

Sci Rep

0

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With the rapid development of artificial intelligence and data science, Dynamic Bayesian Network (DBN), as an effective probabilistic graphical model, has been widely used in many engineering fields. And swarm intelligence algorithm is an optimization algorithm based on natural selection with the characteristics of distributed, self-organization and robustness. By applying the high-performance swarm intelligence algorithm to DBN structure learning, we can fully utilize the algorithm's global search capability to effectively process time-based data, improve the efficiency of network generation and the accuracy of network structure. This study proposes an improved bacterial foraging optimization algorithm (IBFO-A) to solve the problems of random step size, limited group communication, and the inability to maintain a balance between global and local searching. The IBFO-A algorithm framework comprises four layers. First, population initialization is achieved using a logistics-sine chaotic mapping strategy as the basis for global optimization. Second, the activity strategy of a colony foraging trend is constructed by combining the exploration phase of the Osprey optimization algorithm. Subsequently, the strategy of bacterial colony propagation is improved using a "genetic" approach and the Multi-point crossover operator. Finally, the elimination-dispersal activity strategy is employed to escape the local optimal solution. To solve the problem of complex DBN learning structures due to the introduction of time information, a DBN structure learning method called IBFO-D, which is based on the IBFO-A algorithm framework, is proposed. IBFO-D determines the edge direction of the structure by combining the dynamic K2 scoring function, the designed V-structure orientation rule, and the trend activity strategy. Then, according to the improved reproductive activity strategy, the concept of "survival of the fittest" is applied to the network candidate solution while maintaining species diversity. Finally, the global optimal network structure with the highest score is obtained based on the elimination-dispersal activity strategy. Multiple tests and comparison experiments were conducted on 10 sets of benchmark test functions, two non-temporal and temporal data types, and six data samples of two benchmark 2T-BN networks to evaluate and analyze the optimization performance and structure learning ability of the proposed algorithm under various data types. The experimental results demonstrated that IBFO-A exhibits good convergence, stability, and accuracy, whereas IBFO-D is an effective approach for learning DBN structures from data and has practical value for engineering applications.

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“…Furthermore, DBNs have a broad range of engineering applications, such as managing transcriptional regulatory relationships between cancer genes 3 , identifying connectivity issues between human brain regions through high-order DBNs using functional magnetic resonance imaging time series data 4 , and analyzing the vascularization in the formation process of engineered tissues, aiming to enhance the accuracy of predicting future time steps and ensuring an acceptable uncertainty in forecasting the future progress of the organization 5 . Integrating structural prediction methods, such as mutual information and maximum information coefficient into the DBN model enhances the efficiency and scale of gene regulatory network reconstruction 6 .…”

Section: Introductionmentioning

confidence: 99%

Dynamic Bayesian Network Structure Learning Based on an Improved Bacterial Foraging Optimization Algorithm

Meng,

Cong,

Li

et al. 2024

Preprint

0

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With the rapid development of artificial intelligence and data science, Dynamic Bayesian Network (DBN), as an effective probabilistic graphical model, has been widely used in many engineering fields. And swarm intelligence algorithm is an optimization algorithm based on natural selection with the characteristics of distributed, self-organization and robustness. By applying the high-performance swarm intelligence algorithm to DBN structure learning, we can fully utilize the algorithm's global search capability to effectively process time-based data, improve the efficiency of network generation and the accuracy of network structure. This study proposes an improved bacterial foraging optimization algorithm (IBFO-A) to solve the problems of random step size, limited group communication, and the inability to maintain a balance between global and local searching. The IBFO-A algorithm framework comprises four layers. First, population initialization is achieved using a logistics-sine chaotic mapping strategy as the basis for global optimization. Second, the activity strategy of a colony foraging trend is constructed by combining the exploration phase of the Osprey optimization algorithm. Subsequently, the strategy of bacterial colony propagation is improved using a "genetic" approach and the Multi-point crossover operator. Finally, the elimination-dispersal activity strategy is employed to escape the local optimal solution. To solve the problem of complex DBN learning structures due to the introduction of time information, a DBN structure learning method called IBFO-D, which is based on the IBFO-A algorithm framework, is proposed. IBFO-D determines the edge direction of the structure by combining the dynamic K2 scoring function, the designed V-structure orientation rule, and the trend activity strategy. Then, according to the improved reproductive activity strategy, the concept of "survival of the fittest" is applied to the network candidate solution while maintaining species diversity. Finally, the global optimal network structure with the highest score is obtained based on the elimination-dispersal activity strategy. Multiple tests and comparison experiments were conducted on 10 sets of benchmark test functions, two non-temporal and temporal data types, and six data samples of two benchmark 2T-BN networks to evaluate and analyze the optimization performance and structure learning ability of the proposed algorithm under various data types. The experimental results demonstrated that IBFO-A exhibits good convergence, stability, and accuracy, whereas IBFO-D is an effective approach for learning DBN structures from data and has practical value for engineering applications.

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Active inference for dynamic Bayesian networks with an application to tissue engineering

Cited by 7 publications

References 41 publications

Dynamic Bayesian Network Modeling, Learning, and Inference: A Survey

Dynamic Bayesian Network Modeling, Learning, and Inference: A Survey

Dynamic Bayesian network structure learning based on an improved bacterial foraging optimization algorithm

Dynamic Bayesian Network Structure Learning Based on an Improved Bacterial Foraging Optimization Algorithm

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