Article
Automation & Control Systems
Michele Peruzzi, David B. Dunson
Summary: This study proposes a Bayesian multivariate regression model based on spatial multivariate trees (SPAMTREES) that achieves scalability by assuming conditional independence. The model is illustrated using a real climate dataset, demonstrating its effectiveness.
JOURNAL OF MACHINE LEARNING RESEARCH
(2022)
Article
Engineering, Mechanical
P. L. Green, L. J. Devlin, R. E. Moore, R. J. Jackson, J. Li, S. Maskell
Summary: This paper discusses the optimization of the 'L-kernel' in Sequential Monte Carlo samplers to improve performance, resulting in reduced variance of estimates and fewer resampling requirements.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Engineering, Mechanical
Adolphus Lye, Alice Cicirello, Edoardo Patelli
Summary: This tutorial paper reviews the use of advanced Monte Carlo sampling methods in Bayesian model updating for engineering applications, introducing different methods and comparing their performance. Three case studies demonstrate the advantages and limitations of these sampling techniques in parameter identification, posterior distribution sampling, and stochastic identification of model parameters.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2021)
Article
Computer Science, Theory & Methods
Angelos Alexopoulos, Petros Dellaportas, Michalis K. Titsias
Summary: This paper introduces a general framework for constructing estimators with reduced variance for random walk Metropolis and Metropolis-adjusted Langevin algorithms. The proposed method uses the approximate solution of the Poisson equation to produce control variates, achieving variance reduction. Simulated data examples and real data examples verify the effectiveness of the method.
STATISTICS AND COMPUTING
(2023)
Article
Mathematics
Hong-Ding Yang, Yun-Huan Lee, Che-Yang Lin
Summary: This study proposes a spatial Poisson regression model with model parameters inferred by the Bayesian framework to investigate the occurrence rate of Earth-size planets orbiting Sun-like stars. The results show that 46% of Sun-like stars have Earth-size planets with orbital periods of 5-100 days. Additionally, the occurrence rate of Earth analogs hosted by GK dwarf stars (orbital periods of 200-400 days and size 1-2 times Earth radii) is also of interest. After completeness correction, an occurrence rate of 0.18% is obtained based on the proposed methodology.
Article
Computer Science, Theory & Methods
Mekuanint Simeneh Workie, Abebaw Gedef Azene
Summary: Smoking in Ethiopia has various negative impacts on the environment, society, economy, and health. Utilizing a Bayesian model, factors such as age, residence, education level are found to be significantly associated with smoking intensity.
JOURNAL OF BIG DATA
(2021)
Article
Environmental Sciences
Chu-Chih Chen, Yin-Han Wang, Chia-Fang Wu, Chia -Jung Hsieh, Shu-Li Wang, Mei -Lien Chen, Hui-Ju Tsai, Sih-Syuan Li, Chia-Chu Liu, Yi-Chun Tsai, Tusty-Jiuan Hsieh, Ming-Tsang Wu
Summary: This study assessed the relationships between exposure to melamine and DEHP and their combined exposure on early renal injury in pregnant women. The results showed that the current recommended tolerable daily intake levels for these chemicals were much lower than the levels of exposure in pregnant women, especially when considering coexposure to both chemicals.
ENVIRONMENTAL RESEARCH
(2022)
Article
Agriculture, Dairy & Animal Science
M. A. Stephen, C. R. Burke, N. Steele, J. E. Pryce, S. Meier, P. R. Amer, C. V. C. Phyn, D. J. Garrick
Summary: In this study, the genetic and phenotypic relationships between anogenital distance (AGD) and body stature and fertility traits in dairy cattle were characterized. The results showed that AGD is a moderately heritable trait and is associated with reproductive success in lactating cows.
JOURNAL OF DAIRY SCIENCE
(2023)
Article
Statistics & Probability
Jingyu He, P. Richard Hahn
Summary: This article introduces a novel stochastic tree ensemble method called XBART, which combines regularization and stochastic search strategies with efficient recursive partitioning algorithms. It achieves state-of-the-art performance in prediction and function estimation. Simulation studies demonstrate that XBART provides accurate point-wise estimates of the mean function and does so faster than popular alternatives.
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
(2023)
Article
Meteorology & Atmospheric Sciences
Md Wahiduzzaman, Alea Yeasmin, Jing-Jia Luo, Dewan Abdul Quadir, Andre Van Amstel, Kevin Cheung, Chaoxia Yuan
Summary: The study investigates the impact of El Nino-Southern Oscillation (ENSO) on the frequency of Tropical Cyclones (TC) in the Bay of Bengal, utilizing statistical forecasting and Markov Chain Monte Carlo (MCMC) simulation to approximate TC occurrence. The research results show significant differences in monthly and seasonal distribution of TC frequency, with improvements in predictive modeling techniques for TC frequency over the region.
Article
Computer Science, Software Engineering
Jouni Helske
Summary: The R package walker extends Bayesian general linear models to handle time-varying effects of explanatory variables. This approach enables the modeling of intervention effects that gradually increase over time, such as changes in tax policy. The algorithm utilizes Hamiltonian Monte Carlo provided by Stan software to marginalize over the regression coefficients in a state space representation of the model, allowing for efficient low-dimensional sampling.
Review
Statistics & Probability
Christopher Nemeth, Paul Fearnhead
Summary: MCMC algorithms are considered the gold standard technique for Bayesian inference, but the computational cost can be prohibitive for large datasets, leading to the development of scalable Monte Carlo algorithms. One type of these algorithms is SGMCMC, which reduces per-iteration cost by utilizing data subsampling techniques.
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
(2021)
Article
Economics
Zijian Zeng, Meng Li
Summary: A Bayesian median autoregressive model was developed for time series forecasting, utilizing time-varying quantile regression at the median and a Laplace error instead of Gaussian error. Model parameters were estimated using Markov chain Monte Carlo, with Bayesian model averaging and model selection used to address model uncertainty. The methods showed favorable predictive performance in real data applications.
INTERNATIONAL JOURNAL OF FORECASTING
(2021)
Article
Multidisciplinary Sciences
Zhiyong Chen, Minghui Chen, Fangyu Ju
Summary: This paper introduces a model method for spatial data using Bayesian P-splines quantile regression, and evaluates the linear and nonlinear effects of covariates on the response. Through simulations and empirical applications, it is demonstrated that this method is more robust and effective compared to other estimators in handling this type of data.
Article
Statistics & Probability
David P. M. Scollnik
Summary: In this article, several alternatives to the continuous exponential-Poisson distribution are considered to account for the occurrence of zeros. Three of these alternatives are modifications of the exponential-Poisson model, while the other models are semi-continuous models with a discrete point mass at zero and a continuous density on positive values. These models are applied to environmental data sets on precipitation, and their Bayesian analyses using MCMC are discussed, including convergence of the MCMC simulations and considerations for model selection.
JOURNAL OF APPLIED STATISTICS
(2022)
Article
Computer Science, Information Systems
Hongqiang Liu, Xinyan Zhu
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
(2016)
Article
Computer Science, Information Systems
Hongqiang Liu, Xinyan Zhu, Dongying Zhang, Zhen Liu
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
(2019)
Article
Computer Science, Information Systems
Lingbo Liu, Ru Wang, Weihe Wendy Guan, Shuming Bao, Hanchen Yu, Xiaokang Fu, Hongqiang Liu
Summary: This study compares human movement data from Chinese social media Weibo and popular location-based service Baidu Map, finding that Weibo data can reveal similar patterns and has higher correlation at the provincial and monthly scale. The study also reveals spatial variations between the two data sources.
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
(2022)