Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.4
2.3
Journals
Mathematics
Special Issues
Contemporary Bayesian Analysis: Methods and Applications
mathematics-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Mathematics Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Contemporary Bayesian Analysis: Methods and Applications

A Special Issue of Mathematics (ISSN 2227-7390) belonging to the section "D1: Probability and Statistics".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 3422

Editors

Dr. Nuttanan Wichitaksorn

E-Mail Website
Guest Editor
Department of Mathematical Sciences, Auckland University of Technology, Auckland 1010, New Zealand
Interests: Bayesian econometrics and statistics with applications to economics, finance, health science, and social science, and time series analysis and forecasting
Dr. Mengheng Li

E-Mail Website
Guest Editor
1. Economics Discipline Group, University of Technology Sydney, Ultimo, Sydney, NSW 2007, Australia
2. Centre for Applied Macroeconomic Analysis, Australian National University, Canberra, ACT 2601, Australia
Interests: Bayesian statistics; time series econometrics; high-dimensional methods; empirical macroeconomics

Special Issue Information

Dear Colleagues,

We invite submissions to a Special Issue in the journal Mathematics of MDPI on “Contemporary Bayesian Analysis: Methods and Applications”. This Special Issue recognizes the Bayesian revolution in econometrics, statistics, and data science, especially pertaining to their methods and applications.

Authors with papers using any Bayesian approaches are encouraged to submit their papers or have a discussion with us. Papers with recent methods and applications are more than welcome. We also expect to receive contributions from emerging researchers.

Best regards,

Dr. Nuttanan Wichitaksorn
Dr. Mengheng Li
Guest Editors

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly 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 analysis
  • econometrics and statistics
  • time series analysis and forecasting
  • Bayesian applications
  • Bayesian machine learning

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

43 pages, 532 KB  
Article
From a Hierarchical Dirichlet-Type Construction to the Informative Bayesian Double Bootstrap
by Guadalupe Eunice Campirán García
Mathematics 2026, 14(17), 3192; https://doi.org/10.3390/math14173192 - 4 Sep 2026
Viewed by 493
Abstract
Efron’s double bootstrap and hierarchical Bayesian nonparametric methods have largely developed along separate paths. This paper connects them and uses that connection to motivate a new resampling procedure. We show that a suitable two-level Dirichlet construction, with base measures matched to the data, [...] Read more.
Efron’s double bootstrap and hierarchical Bayesian nonparametric methods have largely developed along separate paths. This paper connects them and uses that connection to motivate a new resampling procedure. We show that a suitable two-level Dirichlet construction, with base measures matched to the data, can approach the classical double bootstrap when its concentration parameters become large, while the hierarchical Dirichlet process of Teh et al. does not share this limit. This distinction identifies the double bootstrap as the endpoint of a construction of hierarchical Dirichlet type—though not of the hierarchical Dirichlet process itself—and as the boundary of a broader family of two-level resampling methods. Moving away from that boundary leads to the Informative Bayesian Double Bootstrap (IBDB). The method introduces prior information at the first stage while keeping the second stage focused on calibration, as in the classical double bootstrap. We also establish finite-concentration bounds describing how the proposed construction differs from its classical counterpart and when it can move beyond the support of the observed data. In simulations against four competing methods, the IBDB performs best for tail-sensitive quantities and heavy-tailed settings, while it tends to over-cover simple location parameters. Similar patterns appear in the Danish fire-insurance and Siemens equity-loss examples. Its main advantage is improved calibration through interval repositioning rather than simply wider intervals. The gains are most relevant when sample information is limited. Full article
(This article belongs to the Special Issue Contemporary Bayesian Analysis: Methods and Applications)
►▼ Show Figures

Figure 1

20 pages, 308 KB  
Article
Objective Bayesian Inference for Differential Effects in Unequal-Variance Two-Sample Normal Models
by Sang Gil Kang and Yongku Kim
Mathematics 2026, 14(15), 2761; https://doi.org/10.3390/math14152761 - 3 Aug 2026
Viewed by 363
Abstract
In this paper, we develop a unified objective Bayesian framework for inference on the differential effect in the unequal-variance two-sample normal model. Although general theories of objective priors are well established, the higher-order matching properties of priors for this specific parameter have not [...] Read more.
In this paper, we develop a unified objective Bayesian framework for inference on the differential effect in the unequal-variance two-sample normal model. Although general theories of objective priors are well established, the higher-order matching properties of priors for this specific parameter have not been fully characterized. Using a model-specific orthogonal parametrization, we derive reference priors and first- and second-order probability matching priors. The main methodological contribution is to show that the proposed second-order matching prior simultaneously satisfies posterior-quantile, alternative-coverage, highest posterior density, cumulative distribution function, and conditional-likelihood-ratio matching criteria. In contrast, the reference priors considered in this study satisfy only the first-order matching criterion. We also establish general conditions for posterior propriety under a broad class of noninformative priors. Simulation studies show that the proposed second-order matching prior generally provides frequentist coverage closer to the nominal levels than the reference priors, including in small-sample and unequal-variance settings. These results provide a theoretically justified and practically useful default prior for objective Bayesian inference on differential effects. Full article
(This article belongs to the Special Issue Contemporary Bayesian Analysis: Methods and Applications)
28 pages, 1641 KB  
Article
Bayesian Estimation of R-Vine Copula with Gaussian-Mixture GARCH Margins: An MCMC and Machine Learning Comparison
by Rewat Khanthaporn and Nuttanan Wichitaksorn
Mathematics 2025, 13(23), 3886; https://doi.org/10.3390/math13233886 - 4 Dec 2025
Viewed by 1721
Abstract
This study proposes Bayesian estimation of multivariate regular vine (R-vine) copula models with generalized autoregressive conditional heteroskedasticity (GARCH) margins modeled by Gaussian-mixture distributions. The Bayesian estimation approach includes Markov chain Monte Carlo and variational Bayes with data augmentation. Although R-vines typically involve computationally [...] Read more.
This study proposes Bayesian estimation of multivariate regular vine (R-vine) copula models with generalized autoregressive conditional heteroskedasticity (GARCH) margins modeled by Gaussian-mixture distributions. The Bayesian estimation approach includes Markov chain Monte Carlo and variational Bayes with data augmentation. Although R-vines typically involve computationally intensive procedures limiting their practical use, we address this challenge through parallel computing techniques. To demonstrate our approach, we employ thirteen bivariate copula families within an R-vine pair-copula construction, applied to a large number of marginal distributions. The margins are modeled as exponential-type GARCH processes with intertemporal capital asset pricing specifications, using a mixture of Gaussian and generalized Pareto distributions. Results from an empirical study involving 100 financial returns confirm the effectiveness of our approach. Full article
(This article belongs to the Special Issue Contemporary Bayesian Analysis: Methods and Applications)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop