Bayesian Network and Signal Processing
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".
Deadline for manuscript submissions: 31 July 2026 | Viewed by 808
Special Issue Editor
Interests: dynamic system modeling; causal models; network models; dynamic Bayesian network; complex system models
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Bayesian networks have emerged as a cornerstone for modeling uncertainty and capturing complex dependencies in signal processing applications. These probabilistic graphical models enable robust inference in scenarios ranging from speech enhancement and biomedical signal analysis to adaptive filtering and network optimization. Despite their theoretical appeal, real-world deployment faces challenges such as scalability for high-dimensional systems, dynamic architectural adaptations, and integration with modern machine learning frameworks. This Special Issue seeks to bridge these gaps by advancing methodological innovations and expanding the frontiers of Bayesian network applications in signal processing.
Challenges and Opportunities
While Bayesian networks offer principled frameworks for uncertainty quantification, their application to nonstationary, large-scale signal processing tasks remains nontrivial. Key challenges include the following:
- Dynamic architectural learning: Real-world systems like gene regulatory networks exhibit time-varying interactions, requiring models that detect structural switches while estimating latent states under evolving noise conditions.
- Computational scalability: Sequential Monte Carlo methods and variational inference techniques must adapt to multi-modal sensor data streams without sacrificing real-time performance.
- Integration with deep learning: Hybrid architectures combining Bayesian networks with neural networks could enhance pattern recognition while preserving interpretability.
- Uncertainty-aware optimization: Bayesian optimization strategies show promise for hyperparameter tuning in nonlinear systems but require efficient surrogate models for high-dimensional spaces.
Scope of Contributions
This Special Issue invites original research addressing these challenges through theoretical advancements and practical implementations. Topics of interest include, but are not limited to, the following:
- Advanced inference algorithms: Local Gibbs sampling, stochastic gradient MCMC, and distributed variational methods for large-scale networks.
- Dynamic network architectures: Switching models for time-varying systems in speech processing, biomedical engineering, and financial signal analysis.
- Cross-domain integration: Bayesian tensor decomposition, quantum-inspired networks, and blockchain-secured distributed inference frameworks.
- Real-world applications: Case studies in acoustic echo cancelation, environmentally robust speech recognition, and multi-omics data fusion.
Submissions should emphasize methodological rigor while demonstrating tangible benefits over conventional approaches. We particularly encourage interdisciplinary studies that translate Bayesian network theory into deployable signal processing solutions for healthcare, telecommunications, and IoT systems. By fostering dialog between theorists and practitioners, this Special Issue aims to solidify Bayesian networks as a transformative tool for 21st-century signal processing challenges.
Dr. Carlos Dias Maciel
Guest Editor
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- Bayesian network
- large-scale models
- quantum-inspired network
- distributed inference
- probabilistic models
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