Developments and Applications of Markov Chain Monte Carlo in Bayesian Inference
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Statistical Physics".
Deadline for manuscript submissions: 28 February 2025 | Viewed by 3736
Special Issue Editors
Interests: Bayesian methods; Markov chain Monte Carlo methods and applications
2. Lithuanian Energy Institute, Breslaujos St. 3, 44403 Kaunas, Lithuania
Interests: probabilistic risk assessment; complex systems; Bayesian inference; artificial intelligence; big data analytics; data mining; machine learning; artificial neural networks
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Markov Chain Monte Carlo (MCMC) algorithms are now widely applied in Bayesian statistics for sampling from the posterior distribution of all unknown quantities in a model, for which direct sampling would be difficult. There are many situations, however, where it is impractical or even impossible to draw the samples, e.g., with massive datasets or in the case of intractable posterior and likelihood models. Further, the efficiency and applicability of MCMC depend on how the underlying issue of sampling is taken into account when designing the transition kernel, especially for target distributions with complex dependence structures or with time dependence. In this case, the practical implementation, application, and accuracy of relevant result estimates may suffer from a very slow and sparse exploration of the target distribution.
This Special Issue invites the submission of papers that aim to advance computational developments and innovations in Bayesian statistics, with particular emphasis on Markov chain Monte Carlo methods and their application variants. Papers are expected to contribute to the design of efficient methods and algorithms or improve existing ones with possible demonstrations in challenging applications (e.g., in multidisciplinary technology, industry, or health science). Therefore, this Special Issue welcomes both novel methodological and application-focused contributions to the area of MCMC. Thus, we are seeking contributions and novel MCMC applications, including, but not limited to, the following topics: MCMC in Bayesian inference, Bayesian distributional regression and Bayesian latent class models, MCMC application for probabilistic assessment, uncertainty and sensitivity analysis, anomaly detection, reliability and safety estimation, extreme event analysis, testing or detection quality estimation, big data analytics, information integration, and data fusion applications.
Dr. Ricardo Sandes Ehlers
Prof. Dr. Robertas Alzbutas
Guest Editors
Manuscript Submission Information
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Keywords
- Bayesian statistics
- Markov chain Monte Carlo
- Bayesian inference applications
- Bayesian distributional regression
- Bayesian latent class models
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Planned Papers
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
