Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Article Types

Countries / Regions

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Search Results (180)

Search Parameters:
Keywords = Bayesian Markov Chain Monte Carlo (MCMC)

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
16 pages, 882 KiB  
Article
MatBYIB: A MATLAB-Based Toolkit for Parameter Estimation of Eccentric Gravitational Waves from EMRIs
by Genliang Li, Shujie Zhao, Huaike Guo, Jingyu Su and Zhenheng Lin
Universe 2025, 11(8), 259; https://doi.org/10.3390/universe11080259 - 6 Aug 2025
Abstract
Accurate parameter estimation is essential for gravitational wave data analysis. In extreme mass-ratio inspiral binary systems, orbital eccentricity is a critical parameter for parameter estimation. However, the current software for the parameter estimation of the gravitational wave often neglects the direct estimation of [...] Read more.
Accurate parameter estimation is essential for gravitational wave data analysis. In extreme mass-ratio inspiral binary systems, orbital eccentricity is a critical parameter for parameter estimation. However, the current software for the parameter estimation of the gravitational wave often neglects the direct estimation of orbital eccentricity. To fill this gap, we have developed the MatBYIB, a MATLAB-based software (Version 1.0) package for the parameter estimation of the gravitational wave with arbitrary eccentricity. The MatBYIB employs the Analytical Kludge waveform as a computationally efficient signal generator and computes parameter uncertainties via the Fisher Information Matrix and the Markov Chain Monte Carlo. For Bayesian inference, we implement the Metropolis–Hastings algorithm to derive posterior distributions. To guarantee convergence, the Gelman–Rubin convergence criterion (the Potential Scale Reduction Factor R^) is used to determine sampling adequacy, with MatBYIB dynamically increasing the sample size until R^<1.05 for all parameters. Our results demonstrate strong agreement between predictions based on the Fisher Information Matrix and full MCMC sampling. This program is user-friendly and allows for the estimation of the gravitational wave parameters with arbitrary eccentricity on standard personal computers. Full article
Show Figures

Figure 1

26 pages, 657 KiB  
Article
Bayesian Inference for Copula-Linked Bivariate Generalized Exponential Distributions: A Comparative Approach
by Carlos A. dos Santos, Saralees Nadarajah, Fernando A. Moala, Hassan S. Bakouch and Shuhrah Alghamdi
Axioms 2025, 14(8), 574; https://doi.org/10.3390/axioms14080574 - 25 Jul 2025
Viewed by 173
Abstract
This paper addresses the limitations of existing bivariate generalized exponential (GE) distributions for modeling lifetime data, which often exhibit rigid dependence structures or non-GE marginals. To overcome these limitations, we introduce four new bivariate GE distributions based on the Farlie–Gumbel–Morgenstern, Gumbel–Barnett, Clayton, and [...] Read more.
This paper addresses the limitations of existing bivariate generalized exponential (GE) distributions for modeling lifetime data, which often exhibit rigid dependence structures or non-GE marginals. To overcome these limitations, we introduce four new bivariate GE distributions based on the Farlie–Gumbel–Morgenstern, Gumbel–Barnett, Clayton, and Frank copulas, which allow for more flexible modeling of various dependence structures. We employ a Bayesian framework with Markov Chain Monte Carlo (MCMC) methods for parameter estimation. A simulation study is conducted to evaluate the performance of the proposed models, which are then applied to a real-world dataset of electrical treeing failures. The results from the data application demonstrate that the copula-based models, particularly the one derived from the Frank copula, provide a superior fit compared to existing bivariate GE models. This work provides a flexible and robust framework for modeling dependent lifetime data. Full article
Show Figures

Figure 1

16 pages, 666 KiB  
Article
Bayesian Analysis of the Maxwell Distribution Under Progressively Type-II Random Censoring
by Rajni Goel, Mahmoud M. Abdelwahab and Mustafa M. Hasaballah
Axioms 2025, 14(8), 573; https://doi.org/10.3390/axioms14080573 - 25 Jul 2025
Viewed by 178
Abstract
Accurate modeling of product lifetimes is vital in reliability analysis and engineering to ensure quality and maintain competitiveness. This paper proposes the progressively randomly censored Maxwell distribution, which incorporates both progressive Type-II and random censoring within the Maxwell distribution framework. The model allows [...] Read more.
Accurate modeling of product lifetimes is vital in reliability analysis and engineering to ensure quality and maintain competitiveness. This paper proposes the progressively randomly censored Maxwell distribution, which incorporates both progressive Type-II and random censoring within the Maxwell distribution framework. The model allows for the planned removal of surviving units at specific stages of an experiment, accounting for both deliberate and random censoring events. It is assumed that survival and censoring times each follow a Maxwell distribution, though with distinct parameters. Both frequentist and Bayesian approaches are employed to estimate the model parameters. In the frequentist approach, maximum likelihood estimators and their corresponding confidence intervals are derived. In the Bayesian approach, Bayes estimators are obtained using an inverse gamma prior and evaluated through a Markov Chain Monte Carlo (MCMC) method under the squared error loss function (SELF). A Monte Carlo simulation study evaluates the performance of the proposed estimators. The practical relevance of the methodology is demonstrated using a real data set. Full article
Show Figures

Figure 1

11 pages, 961 KiB  
Article
Viscous Cosmology in f(Q,Lm) Gravity: Insights from CC, BAO, and GRB Data
by Dheeraj Singh Rana, Sai Swagat Mishra, Aaqid Bhat and Pradyumn Kumar Sahoo
Universe 2025, 11(8), 242; https://doi.org/10.3390/universe11080242 - 23 Jul 2025
Viewed by 228
Abstract
In this article, we investigate the influence of viscosity on the evolution of the cosmos within the framework of the newly proposed f(Q,Lm) gravity. We have considered a linear functional form [...] Read more.
In this article, we investigate the influence of viscosity on the evolution of the cosmos within the framework of the newly proposed f(Q,Lm) gravity. We have considered a linear functional form f(Q,Lm)=αQ+βLm with a bulk viscous coefficient ζ=ζ0+ζ1H for our analysis and obtained exact solutions to the field equations associated with a flat FLRW metric. In addition, we utilized Cosmic Chronometers (CC), CC + BAO, CC + BAO + GRB, and GRB data samples to determine the constrained values of independent parameters in the derived exact solution. The likelihood function and the Markov Chain Monte Carlo (MCMC) sampling technique are combined to yield the posterior probability using Bayesian statistical methods. Furthermore, by comparing our results with the standard cosmological model, we found that our considered model supports the acceleration of the universe in late time. Full article
Show Figures

Figure 1

25 pages, 10024 KiB  
Article
Forecasting with a Bivariate Hysteretic Time Series Model Incorporating Asymmetric Volatility and Dynamic Correlations
by Hong Thi Than
Entropy 2025, 27(7), 771; https://doi.org/10.3390/e27070771 - 21 Jul 2025
Viewed by 239
Abstract
This study explores asymmetric volatility structures within multivariate hysteretic autoregressive (MHAR) models that incorporate conditional correlations, aiming to flexibly capture the dynamic behavior of global financial assets. The proposed framework integrates regime switching and time-varying delays governed by a hysteresis variable, enabling the [...] Read more.
This study explores asymmetric volatility structures within multivariate hysteretic autoregressive (MHAR) models that incorporate conditional correlations, aiming to flexibly capture the dynamic behavior of global financial assets. The proposed framework integrates regime switching and time-varying delays governed by a hysteresis variable, enabling the model to account for both asymmetric volatility and evolving correlation patterns over time. We adopt a fully Bayesian inference approach using adaptive Markov chain Monte Carlo (MCMC) techniques, allowing for the joint estimation of model parameters, Value-at-Risk (VaR), and Marginal Expected Shortfall (MES). The accuracy of VaR forecasts is assessed through two standard backtesting procedures. Our empirical analysis involves both simulated data and real-world financial datasets to evaluate the model’s effectiveness in capturing downside risk dynamics. We demonstrate the application of the proposed method on three pairs of daily log returns involving the S&P500, Bank of America (BAC), Intercontinental Exchange (ICE), and Goldman Sachs (GS), present the results obtained, and compare them against the original model framework. Full article
(This article belongs to the Section Information Theory, Probability and Statistics)
Show Figures

Figure 1

36 pages, 1465 KiB  
Article
USV-Affine Models Without Derivatives: A Bayesian Time-Series Approach
by Malefane Molibeli and Gary van Vuuren
J. Risk Financial Manag. 2025, 18(7), 395; https://doi.org/10.3390/jrfm18070395 - 17 Jul 2025
Viewed by 263
Abstract
We investigate the affine term structure models (ATSMs) with unspanned stochastic volatility (USV). Our aim is to test their ability to generate accurate cross-sectional behavior and time-series dynamics of bond yields. Comparing the restricted models and those with USV, we test whether they [...] Read more.
We investigate the affine term structure models (ATSMs) with unspanned stochastic volatility (USV). Our aim is to test their ability to generate accurate cross-sectional behavior and time-series dynamics of bond yields. Comparing the restricted models and those with USV, we test whether they produce both reasonable estimates for the short rate variance and cross-sectional fit. Essentially, a joint approach from both time series and options data for estimating risk-neutral dynamics in ATSMs should be followed. Due to the scarcity of derivative data in emerging markets, we estimate the model using only time-series of bond yields. A Bayesian estimation approach combining Markov Chain Monte Carlo (MCMC) and the Kalman filter is employed to recover the model parameters and filter out latent state variables. We further incorporate macro-economic indicators and GARCH-based volatility as external validation of the filtered latent volatility process. The A1(4)USV performs better both in and out of sample, even though the issue of a tension between time series and cross-section remains unresolved. Our findings suggest that even without derivative instruments, it is possible to identify and interpret risk-neutral dynamics and volatility risk using observable time-series data. Full article
(This article belongs to the Section Financial Markets)
Show Figures

Figure 1

30 pages, 3032 KiB  
Article
A Bayesian Additive Regression Trees Framework for Individualized Causal Effect Estimation
by Lulu He, Lixia Cao, Tonghui Wang, Zhenqi Cao and Xin Shi
Mathematics 2025, 13(13), 2195; https://doi.org/10.3390/math13132195 - 4 Jul 2025
Viewed by 400
Abstract
In causal inference research, accurate estimation of individualized treatment effects (ITEs) is at the core of effective intervention. This paper proposes a dual-structure ITE-estimation model based on Bayesian Additive Regression Trees (BART), which constructs independent BART sub-models for the treatment and control groups, [...] Read more.
In causal inference research, accurate estimation of individualized treatment effects (ITEs) is at the core of effective intervention. This paper proposes a dual-structure ITE-estimation model based on Bayesian Additive Regression Trees (BART), which constructs independent BART sub-models for the treatment and control groups, estimates ITEs using the potential outcome framework and enhances posterior stability and estimation reliability through Markov Chain Monte Carlo (MCMC) sampling. Based on psychological stress questionnaire data from graduate students, the study first integrates BART with the Shapley value method to identify employment pressure as a key driving factor and reveals substantial heterogeneity in ITEs across subgroups. Furthermore, the study constructs an ITE model using a dual-structured BART framework (BART-ITE), where employment pressure is defined as the treatment variable. Experimental results show that the model performs well in terms of credible interval width and ranking ability, demonstrating superior heterogeneity detection and individual-level sorting. External validation using both the Bootstrap method and matching-based pseudo-ITE estimation confirms the robustness of the proposed model. Compared with mainstream meta-learning methods such as S-Learner, X-Learner and Bayesian Causal Forest, the dual-structure BART-ITE model achieves a favorable balance between root mean square error and bias. In summary, it offers clear advantages in capturing ITE heterogeneity and enhancing estimation reliability and individualized decision-making. Full article
(This article belongs to the Special Issue Bayesian Learning and Its Advanced Applications)
Show Figures

Figure 1

24 pages, 2253 KiB  
Article
Modeling Spatial Data with Heteroscedasticity Using PLVCSAR Model: A Bayesian Quantile Regression Approach
by Rongshang Chen and Zhiyong Chen
Entropy 2025, 27(7), 715; https://doi.org/10.3390/e27070715 - 1 Jul 2025
Viewed by 320
Abstract
Spatial data not only enables smart cities to visualize, analyze, and interpret data related to location and space, but also helps departments make more informed decisions. We apply a Bayesian quantile regression (BQR) of the partially linear varying coefficient spatial autoregressive (PLVCSAR) model [...] Read more.
Spatial data not only enables smart cities to visualize, analyze, and interpret data related to location and space, but also helps departments make more informed decisions. We apply a Bayesian quantile regression (BQR) of the partially linear varying coefficient spatial autoregressive (PLVCSAR) model for spatial data to improve the prediction of performance. It can be used to capture the response of covariates to linear and nonlinear effects at different quantile points. Through an approximation of the nonparametric functions with free-knot splines, we develop a Bayesian sampling approach that can be applied by the Markov chain Monte Carlo (MCMC) approach and design an efficient Metropolis–Hastings within the Gibbs sampling algorithm to explore the joint posterior distributions. Computational efficiency is achieved through a modified reversible-jump MCMC algorithm incorporating adaptive movement steps to accelerate chain convergence. The simulation results demonstrate that our estimator exhibits robustness to alternative spatial weight matrices and outperforms both quantile regression (QR) and instrumental variable quantile regression (IVQR) in a finite sample at different quantiles. The effectiveness of the proposed model and estimation method is demonstrated by the use of real data from the Boston median house price. Full article
(This article belongs to the Special Issue Bayesian Hierarchical Models with Applications)
Show Figures

Figure 1

21 pages, 2735 KiB  
Article
Price Volatility Spillovers in Energy Supply Chains: Empirical Evidence from China
by Lei Wang, Yu Sun and Jining Wang
Energies 2025, 18(12), 3204; https://doi.org/10.3390/en18123204 - 18 Jun 2025
Viewed by 355
Abstract
Based on the theoretical framework of Multivariate Stochastic Volatility (MSV), this paper combines the Dynamic Generalized Correlation (DGC) model with the t-distribution, establishes the DGC-t-MSV model, and employs the Markov Chain Monte Carlo (MCMC) algorithm based on the Bayesian principle for efficient estimation [...] Read more.
Based on the theoretical framework of Multivariate Stochastic Volatility (MSV), this paper combines the Dynamic Generalized Correlation (DGC) model with the t-distribution, establishes the DGC-t-MSV model, and employs the Markov Chain Monte Carlo (MCMC) algorithm based on the Bayesian principle for efficient estimation to investigate the price volatility spillover effects in China’s energy supply chains. The results of this study indicate the following: (1) The upstream crude oil spot price has a positive spillover effect on the midstream freight price. The downstream diesel market price, 92 gasoline market price, and 95 gasoline market price all exert positive volatility spillovers on the midstream crude oil freight price. (2) The volatility spillover effect between the upstream power coal price and the midstream coal freight price exhibits unidirectionality, and the volatility is transmitted from the power coal price to the coal freight price. (3) The upstream natural gas price and the midstream liquefied natural gas market price display asymmetric characteristics. Among them, the upstream natural gas price has a unidirectional and more pronounced positive volatility spillover effect on the midstream liquefied natural gas market price. Full article
Show Figures

Figure 1

27 pages, 993 KiB  
Article
Statistical Inference of Inverse Weibull Distribution Under Joint Progressive Censoring Scheme
by Jinchen Xiang, Yuanqi Wang and Wenhao Gui
Symmetry 2025, 17(6), 829; https://doi.org/10.3390/sym17060829 - 26 May 2025
Viewed by 358
Abstract
In recent years, there has been an increasing interest in the application of progressive censoring as a means to reduce both cost and experiment duration. In the absence of explanatory variables, the present study employs a statistical inference approach for the inverse Weibull [...] Read more.
In recent years, there has been an increasing interest in the application of progressive censoring as a means to reduce both cost and experiment duration. In the absence of explanatory variables, the present study employs a statistical inference approach for the inverse Weibull distribution, using a progressive type II censoring strategy with two independent samples. The article expounds on the maximum likelihood estimation method, utilizing the Fisher information matrix to derive approximate confidence intervals. Moreover, interval estimations are computed by the bootstrap method. We explore the application of Bayesian methods for estimating model parameters under both the squared error and LINEX loss functions. The Bayesian estimates and corresponding credible intervals are calculated via Markov chain Monte Carlo (MCMC). Finally, comprehensive simulation studies and real data analysis are carried out to validate the precision of the proposed estimation methods. Full article
(This article belongs to the Section Mathematics)
Show Figures

Figure 1

31 pages, 4528 KiB  
Article
Probabilistic Prediction Model for Ultimate Conditions Under Compression of FRP-Wrapped Concrete Columns Based on Bayesian Inference
by Feng Cao, Ran Zhu, Jun-Xing Zheng, Hai-Bin Huang and Dong Liang
Buildings 2025, 15(10), 1720; https://doi.org/10.3390/buildings15101720 - 19 May 2025
Viewed by 496
Abstract
The compressive strength and ultimate strain of FRP-confined concrete cylinders are the key indicators for evaluating their mechanical properties. Accurate prediction of compressive strength and ultimate strain is essential for reliability analysis and design of such components. However, the existing ultimate condition under [...] Read more.
The compressive strength and ultimate strain of FRP-confined concrete cylinders are the key indicators for evaluating their mechanical properties. Accurate prediction of compressive strength and ultimate strain is essential for reliability analysis and design of such components. However, the existing ultimate condition under compression models lack sufficient prediction accuracy, and the results exhibit significant uncertainty. This study proposes a Bayesian model updating method based on Markov Chain Monte Carlo (MCMC) sampling to improve the prediction accuracy of the ultimate condition under compression for FRP-confined concrete cylinders and to quantify the uncertainty of the prediction results. First of all, 1016 sets of experimental data on the ultimate condition under compression of FRP-confined concrete cylinders from previous studies were collected. Subsequently, the probabilistic updating model and evaluation system were established based on Bayesian parameter estimation principle, MCMC sampling, WAIC, and DIC. Then, several representative empirical models for predicting the ultimate condition under compression are selected, and their prediction performance is evaluated using the experimental data. Finally, a Bayesian updating problem is established for typical ultimate condition under compression models, and the posterior distributions of model parameters are obtained using MCMC sampling to select the best model, and the prediction performance of the optimal model is assessed using the experimental data. The results show that, compared with existing empirical models, the Bayesian inference-based probabilistic calculation model provides predictions closer to the experimental values, while also reasonably quantifying the uncertainty of the ultimate condition under compression prediction. Full article
Show Figures

Figure 1

25 pages, 657 KiB  
Article
Bitcoin Price Regime Shifts: A Bayesian MCMC and Hidden Markov Model Analysis of Macroeconomic Influence
by Vaiva Pakštaitė, Ernestas Filatovas, Mindaugas Juodis and Remigijus Paulavičius
Mathematics 2025, 13(10), 1577; https://doi.org/10.3390/math13101577 - 10 May 2025
Viewed by 2919
Abstract
Bitcoin’s role in global finance has rapidly expanded with increasing institutional participation, prompting new questions about its linkage to macroeconomic variables. This study thoughtfully integrates a Bayesian Markov Chain Monte Carlo (MCMC) covariate selection process within homogeneous and non-homogeneous Hidden Markov Models (HMMs) [...] Read more.
Bitcoin’s role in global finance has rapidly expanded with increasing institutional participation, prompting new questions about its linkage to macroeconomic variables. This study thoughtfully integrates a Bayesian Markov Chain Monte Carlo (MCMC) covariate selection process within homogeneous and non-homogeneous Hidden Markov Models (HMMs) to analyze 16 macroeconomic and Bitcoin-specific factors from 2016 to 2024. The proposed method integrates likelihood penalties to refine variable selection and employs a rolling-window bootstrap procedure for 1-, 5-, and 30-step-ahead forecasting. Results indicate a fundamental shift: while early Bitcoin pricing was primarily driven by technical and supply-side factors (e.g., halving cycles, trading volume), later periods exhibit stronger ties to macroeconomic indicators such as exchange rates and major stock indices. Heightened volatility aligns with significant events—including regulatory changes and institutional announcements—underscoring Bitcoin’s evolving market structure. These findings demonstrate that integrating Bayesian MCMC within a regime-switching model provides robust insights into Bitcoin’s deepening connection with traditional financial forces. Full article
Show Figures

Figure 1

20 pages, 595 KiB  
Article
Learning Gaussian Bayesian Network from Censored Data Subject to Limit of Detection by the Structural EM Algorithm
by Ping-Feng Xu, Shanyi Lin, Qian-Zhen Zheng and Man-Lai Tang
Mathematics 2025, 13(9), 1482; https://doi.org/10.3390/math13091482 - 30 Apr 2025
Viewed by 336
Abstract
A Bayesian network offers powerful knowledge representations for independence, conditional independence and causal relationships among variables in a given domain. Despite its wide application, the detection limits of modern measurement technologies make the use of the Bayesian networks theoretically unfounded, even when the [...] Read more.
A Bayesian network offers powerful knowledge representations for independence, conditional independence and causal relationships among variables in a given domain. Despite its wide application, the detection limits of modern measurement technologies make the use of the Bayesian networks theoretically unfounded, even when the assumption of a multivariate Gaussian distribution is satisfied. In this paper, we introduce the censored Gaussian Bayesian network (GBN), an extension of GBNs designed to handle left- and right-censored data caused by instrumental detection limits. We further propose the censored Structural Expectation-Maximization (cSEM) algorithm, an iterative score-and-search framework that integrates Monte Carlo sampling in the E-step for efficient expectation computation and employs the iterative Markov chain Monte Carlo (MCMC) algorithm in the M-step to refine the network structure and parameters. This approach addresses the non-decomposability challenge of censored-data likelihoods. Through simulation studies, we illustrate the superior performance of the cSEM algorithm compared to the existing competitors in terms of network recovery when censored data exist. Finally, the proposed cSEM algorithm is applied to single-cell data with censoring to uncover the relationships among variables. The implementation of the cSEM algorithm is available on GitHub. Full article
Show Figures

Figure 1

21 pages, 4335 KiB  
Article
Advancing Decision-Making in AI Through Bayesian Inference and Probabilistic Graphical Models
by Mohammed Atef Abdallah
Symmetry 2025, 17(5), 635; https://doi.org/10.3390/sym17050635 - 23 Apr 2025
Cited by 1 | Viewed by 1037
Abstract
The navigation of autonomous vehicles should be accurate and reliable to navigate safely in changing and unpredictable conditions. This paper proposes an advanced autonomous vehicle navigation framework that integrates probabilistic graphical models, Markov Chain Monte Carlo methods, and Bayesian optimization to enable reliable, [...] Read more.
The navigation of autonomous vehicles should be accurate and reliable to navigate safely in changing and unpredictable conditions. This paper proposes an advanced autonomous vehicle navigation framework that integrates probabilistic graphical models, Markov Chain Monte Carlo methods, and Bayesian optimization to enable reliable, real-time decision-making in uncertain environments. Due to dynamic and unpredictable surroundings, autonomous navigation is highly challenged in uncertainty quantification and adaptive parameter tuning. By leveraging PGMs, the framework can first determine probabilistic dependencies between critical variables, i.e., nodes and edges, such as vehicle speed, obstacle proximity, and environmental factors, to create a robust foundation for situational awareness. Then, Bayesian inference is obtained using MCMC: the system updates its real-time beliefs as new sensor data become available. The inference layer allows adaptation to unexpected obstacles by revising trajectories or controlling a vehicle’s speed while improving safety and reliability. Finally, Bayesian optimization fine-tunes key parameters within the system, such as sensor thresholds and control variables, maximizing efficiency without exhaustive manual tuning of these parameters. Using a multi-sensor data source with images, LiDAR, radar, and annotated environmental features, the Lyft Level 5 Perception Dataset tested real-world navigation scenarios against the framework. This proposed framework’s accuracy was around 99.01% and signified good decision-making capabilities for an autonomous vehicle navigating through complex environments with reliable performance. The autonomous vehicle system is also intended to provide improved safety and flexibility in complex environments, promising the development of more resilient and dependable AI-driven solutions for navigation. Full article
(This article belongs to the Section Mathematics)
Show Figures

Figure 1

10 pages, 539 KiB  
Article
Fundamental Parameters and Evolutionary Scenario of HD 327083
by Nadezhda L. Vaidman, Anatoly S. Miroshnichenko, Sergey V. Zharikov, Serik A. Khokhlov, Aldiyar T. Agishev and Berik S. Yermekbayev
Galaxies 2025, 13(3), 47; https://doi.org/10.3390/galaxies13030047 - 22 Apr 2025
Cited by 1 | Viewed by 533
Abstract
In this study, we present refined orbital and fundamental parameters of the Galactic B[e] supergiant binary system HD 327083 using the Bayesian Markov Chain Monte Carlo (MCMC) method applied to the radial velocities data of HD 327083. We found that the system is [...] Read more.
In this study, we present refined orbital and fundamental parameters of the Galactic B[e] supergiant binary system HD 327083 using the Bayesian Markov Chain Monte Carlo (MCMC) method applied to the radial velocities data of HD 327083. We found that the system is well described by a circular orbital model with the mass ratio of the components of q=1.15±0.07. We modeled the evolutionary history of the system using MESA code. Initially, the system was formed by a binary with the orbital period of Porb=108 day, which contained stars with 13.00 ±0.05 M and 11.50±0.05 M masses. They had a relatively slow rotation υrot=0.40±0.13υcrit and provided a strong stellar wind. The current system age is 13.6±0.1 Myr, and the state of the system corresponds to a close filling of the high massive component’s Roche lobe and a beginning of the mass transfer. The mass-transfer event will occur in a short interval of ≲0.1 Myr only. After that, the mass of the post-primary drops to ≈5 M, the post-secondary mass grows until ≈20 M, and the binary will convert to a detached system with a long orbital period of ≈700 days. Full article
(This article belongs to the Special Issue Circumstellar Matter in Hot Star Systems)
Show Figures

Figure 1

Back to TopTop