Article
Biochemical Research Methods
Ying Zhang, Yuxin Song, Jin Gao, Hengyu Zhang, Ning Yang, Runqing Yang
Summary: The study extended the Hi-RRM model into a genome-wide association analysis for longitudinal data, reducing the dimensionality of repeated measurements significantly. The method improved computing efficiency through model transformation and demonstrated its statistical utility through simulation experiments.
BRIEFINGS IN BIOINFORMATICS
(2021)
Article
Biochemical Research Methods
Zhiyu Hao, Jin Gao, Yuxin Song, Runqing Yang, Di Liu
Summary: In this study, a hierarchical mixed model (Hi-LMM) was proposed to estimate genomic breeding values and infer the association between genotypes and phenotypes, effectively correcting confounding factors and improving the statistical power of association analysis.
BRIEFINGS IN BIOINFORMATICS
(2021)
Article
Engineering, Mechanical
Junming Ma, Nani Bai, Yi Zhou, Chengming Lan, Hui Li, B. F. Spencer
Summary: This article proposes the use of generalized hierarchical Bayesian inference for fatigue life prediction based on general multi-parameter Weibull models. The article establishes a three-layer hierarchical Bayesian structure and uses Gibbs sampling to obtain posterior samples for parameters and hyperparameters. The results show that the scatter in fatigue life prediction for the corroded specimens becomes smaller when considering informative priors for the parameters in the Weibull model.
INTERNATIONAL JOURNAL OF FATIGUE
(2022)
Article
Management
Benjamin Holmes, Ian G. Mchale, Kamila Zychaluk
Summary: This paper investigates the decision-making of judges in MMA contests using Bayesian hierarchical models. It finds that judges have personal preferences which may influence the outcome of a match. The paper also applies the concept of variable significance to evaluate the reasonableness of judge's verdicts.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Ophthalmology
Giovanni Montesano, David F. Garway-Heath, Giovanni Ometto, David P. Crabb
Summary: Developed hierarchical Bayesian models to account for the nature of visual field progression data, with censored models showing the smallest bias in rate-of-progression. Results indicate that Bayesian models performed better in terms of Hit-rate and time-to-progression compared to other methods. Account for censoring improved precision of estimates, with minimal effect from accounting for heteroskedasticity.
TRANSLATIONAL VISION SCIENCE & TECHNOLOGY
(2021)
Article
Computer Science, Interdisciplinary Applications
Nikolas Kuschnig, Lukas Vashold
Summary: This paper introduces BVAR, an R package dedicated to estimating Bayesian VAR models with hierarchical prior selection, providing functionalities for a wide range of research problems. The package offers a user-friendly and transparent interface, making Bayesian VAR models accessible for users.
JOURNAL OF STATISTICAL SOFTWARE
(2021)
Article
Environmental Sciences
Gregory L. Britten, Yara Mohajerani, Louis Primeau, Murat Aydin, Catherine Garcia, Wei-Lei Wang, Benoit Pasquier, B. B. Cael, Francois W. Primeau
Summary: Hierarchical Bayesian modeling is increasingly used in environmental science to describe statistical complexities in large compiled datasets, offering benefits such as flexibility, reduction of uncertainty, and incorporation of prior scientific information. Its versatility and feasibility for diverse environmental applications are highlighted, enhanced by recent developments in Markov Chain Monte Carlo algorithms and user-friendly software implementations.
FRONTIERS IN ENVIRONMENTAL SCIENCE
(2021)
Article
Construction & Building Technology
Hoyeon Hwang, Yiyi Chu, Hyejin Eom, Kristen Cetin, Jongho Im
Summary: Data-driven models, such as change point models and artificial neural networks, are often used to predict energy use in single and multiple buildings. A statistical estimation method is proposed to polarize energy use patterns across residential buildings and determine the net energy use pattern. The method involves using a monomolecular growth curve to approximate energy uses of multiple buildings and a Bayesian hierarchical model to combine energy use across targeted buildings.
BUILDING AND ENVIRONMENT
(2021)
Article
Mathematics, Applied
Yingchun Jiang, Wan Li
Summary: The study focuses on random sampling and reconstruction in multiply generated shift-invariant subspaces in mixed Lebesgue spaces. By suitable conditions for the generators, it is proven that sampling stability holds with high probability for functions with energy concentrated on a compact subset when sampling sizes are large enough. Finally, a reconstruction algorithm based on random samples is provided for functions in a finite dimensional subspace.
JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS
(2021)
Article
Computer Science, Artificial Intelligence
Manuele Leonelli, Ramsiya Ramanathan, Rachel L. Wilkerson
Summary: This paper introduces the application of Bayesian networks in complex operational systems, as well as the current software tools for constructing Bayesian networks. It also presents a comprehensive software package called bnmonitor for model-checking of Bayesian networks, and demonstrates its usage through an analysis of a medical dataset.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Ecology
Kenneth F. Kellner, Nicholas L. Fowler, Tyler R. Petroelje, Todd M. Kautz, Dean E. Beyer, Jerrold L. Belant
Summary: Getting unbiased estimates of wildlife distribution and abundance is an important objective in research and management. Fitting occupancy and N-mixture abundance models in a Bayesian framework using Stan has advantages, but can be challenging for many researchers. The ubms package provides an easy-to-use interface for fitting models and analyzing data, potentially expanding the user base for rigorously assessing species distribution and abundance.
METHODS IN ECOLOGY AND EVOLUTION
(2022)
Review
Environmental Sciences
Olivera Stojanovic, Bastian Siegmann, Thomas Jarmer, Gordon Pipa, Johannes Leugering
Summary: Researchers use Bayesian hierarchical models to predict complex phenomena from heterogeneous datasets. They find that this approach helps mitigate the problems caused by heterogeneity and improves the robustness and interpretability of predictive models. One application is the estimation of leaf area index, an important indicator in agronomical modeling.
FRONTIERS IN ENVIRONMENTAL SCIENCE
(2022)
Article
Economics
Tjeerd M. Boonman
Summary: The Global Financial Crisis had a significant impact on portfolio capital flows. Previous literature has shown mixed results on the driving factors of these capital flows, potentially due to the different time periods compared. This study identifies and compares the robust drivers of portfolio capital inflows for 75 countries during two non-overlapping periods using the Bayesian Model Averaging method. The findings indicate that while the combination of global and country-specific factors remains important in both periods, the individual drivers have changed significantly in the post-GFC period, highlighting a greater focus on risk by international investors.
ECONOMIC MODELLING
(2023)
Article
Engineering, Civil
Alvaro Ossandon, Balaji Rajagopalan, William Kleiber
Summary: The semi-Bayesian hierarchical modeling framework combines generalized extreme value distribution and Gaussian multivariate process to analyze precipitation extremes over a large domain. By conducting space-time frequency analysis of seasonal maximum precipitation, the model captures historical variability well and has wide applications in natural resources and infrastructure management.
JOURNAL OF HYDROLOGY
(2021)
Article
Mathematics, Interdisciplinary Applications
Sergio Bacallado, Stefano Favaro, Samuel Power, Lorenzo Trippa
Summary: This paper develops a perfect sampler using the Propp-Wilson algorithm to simulate the posterior distribution of the hierarchical Pitman-Yor process, and evaluates its average running time through extensive simulations. The simulations reveal a significant dependence of running time on the parameters of the model.
Article
Multidisciplinary Sciences
Rebecca Fisher, Jeffrey M. Leis, J. Derek Hogan, David R. Bellwood, Shaun K. Wilson, Suresh D. Job
Summary: This article presents a collation of data on swimming abilities of tropical marine fish larvae and pelagic juveniles, providing valuable information for studying larval swimming performance and other comprehensive research.
Article
Fisheries
Shaun K. Wilson, Christopher J. Fulton, Nicholas A. J. Graham, Rene A. Abesamis, Charlotte Berkstrom, Darren J. Coker, Martial Depczynski, Richard D. Evans, Rebecca Fisher, Jordan Goetze, Andrew Hoey, Thomas H. Holmes, Michel Kulbicki, Mae Noble, James P. W. Robinson, Michael Bradley, Carolina Akerlund, Luke T. Barrett, Abner A. Bucol, Matthew J. Birt, Dinorah H. Chacin, Karen M. Chong-Seng, Linda Eggertsen, Maria Eggertsen, David Ellis, Priscilla T. Y. Leung, Paul K. S. Lam, Joshua van Lier, Paloma A. Matis, Alejandro Perez-Matus, Camilla V. H. Piggott, Ben T. Radford, Stina Tano, Paul Tinkler
Summary: Macroalgal habitats contribute to small-scale tropical reef fisheries to a certain extent, supporting a diversity of fish species. Fish associated with macroalgal habitats account for 24% of the catch, but very few species rely solely on macroalgal or coral habitats. Fish in macroalgal and coral habitats have similar life-history traits, and the vulnerability to fishing decreases as the contribution of macroalgae to the catch increases. The study also shows that macroalgae-associated fish can enhance catch size and diversity, which is important in seascapes where coral reefs are being replaced by macroalgal habitats.
FISH AND FISHERIES
(2022)
Article
Entomology
Daniel Cook, Boyd Tarlinton, James M. McGree, Alethea Blackler, Caroline Hauxwell
Summary: Strength auditing of European honey bee colonies is critical for colony health management. This study evaluates the use of temperature sensing technology in colony strength assessment and identifies key parameters linking temperature to colony strength. The presence of bees in hives significantly affects hive temperature and range, and sensor placement across the width of the hive is important when linking sensor data with colony strength. Statistical models can be used to predict colony strength from temperature sensor data.
JOURNAL OF ECONOMIC ENTOMOLOGY
(2022)
Article
Environmental Sciences
Charlotte Aston, Tim Langlois, Rebecca Fisher, Jacquomo Monk, Brooke Gibbons, Anita Giraldo-Ospina, Emma Lawrence, John Keesing, Ulysse Lebrec, Russ C. Babcock
Summary: This study aimed to investigate the impact of recreational fishing and the feasibility of a newly established no-take zone in Ningaloo Marine Park. The results showed that the distance to the nearest boat ramp was a strong predictor of fished species abundance, and the effect of the no-take zone on fished species abundance was weak but expected to increase over time.
FRONTIERS IN MARINE SCIENCE
(2022)
Article
Engineering, Industrial
Harry Sisley, Guvenc Dik, James McGree, Paul Corry
Summary: This paper examines the challenges of managing the supply chain for seasonal crops in Australia and introduces a supply chain model that manages the production of multiple crops across different regions. By using deterministic mixed integer programming and a heuristic solution method, the proposed model can solve the problem faster and with less deviation in the planning process.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Ecology
Pubudu Thilan Abeysiri Wickrama Liyanaarachchige, Rebecca Fisher, Helen Thompson, Patricia Menendez, James Gilmour, James M. McGree
Summary: This article describes the characteristics of time series data commonly observed in ecological monitoring and proposes methods for modeling and adaptive monitoring in such settings. Analyzing the monitoring data from Scott Reef, it is found that future monitoring designs do not need to prioritize specific locations and that sampling sites can be omitted based on observed disturbances without substantial loss in expected information gain. As the methods developed in this study are generic, this research has the potential to improve ecological monitoring in collecting complex data over time.
ECOLOGY AND EVOLUTION
(2022)
Article
Mathematical & Computational Biology
S. G. J. Senarathne, Werner G. Mueller, James M. McGree
Summary: Model-based geostatistical design involves selecting locations to collect data in order to minimize an expected loss function over all possible locations. The loss function reflects the goal of data collection, which in geostatistical studies is often to minimize prediction uncertainty at unobserved locations. This paper proposes a new approach to this design problem by considering the entropy of model predictions and parameters as part of the loss function. The approach extends to generalized linear spatial models, allowing for experiments with multiple responses.
BIOMETRICAL JOURNAL
(2023)
Article
Environmental Sciences
A. W. L. P. Thilan, P. Menendez, J. M. McGree
Summary: This study developed an approach to assess trends in hard coral cover and evaluate the effectiveness of adaptive designs for estimating such trends in coral reef communities. The findings show that adaptive designs can maintain trends over time with little to no loss in information, even with reduced sampling effort. This research serves to further promote adaptive design methods for efficient and effective ecological monitoring.
Article
Engineering, Civil
I. P. Gustave S. Pariartha, Shubham Aggarwal, Srinivas Rallapalli, Prasanna Egodawatta, James McGree, Ashantha Goonetilleke
Summary: Climate change and urbanization have adverse impacts on rainfall and sea level, contributing to future flood risk. This study presents an innovative flood damage and hazard prediction model that integrates MIKE FLOOD and GIS technology to assess flood scenarios for different time horizons. Results show that changes in rainfall patterns significantly affect the average annual damage caused by flooding. The proposed model can guide decision-makers in assessing future flood management.
JOURNAL OF HYDROLOGY
(2023)
Article
Agronomy
Mahmoud Masoud, Jeff Hsieh, Kate Helmstedt, James McGree, Paul Corry
Summary: Beef production plays a crucial role in Australia's agricultural economy, with an annual agricultural production value of AUD11 billion. The profitability of cattle farms is highly affected by weather conditions and the associated uncertainty in pasture growth, as well as the need to manage stocking rates to prevent overgrazing. Predictive modeling of pasture growth and cattle weight gain can effectively assist producers in managing this challenge.
FOOD AND ENERGY SECURITY
(2023)
Article
Health Policy & Services
Jack Powers, James M. McGree, David Grieve, Ratna Aseervatham, Suzanne Ryan, Paul Corry
Summary: The use of a dynamic priority scoring (DPS) system can prioritize elective surgery patients more equitably based on waiting time and clinical factors, reducing subjectivity and increasing transparency in the waiting list management.
HEALTH CARE MANAGEMENT SCIENCE
(2023)
Article
Computer Science, Artificial Intelligence
Sander J. J. Leemans, James M. McGree, Artem Polyvyanyy, Arthur H. M. ter Hofstede
Summary: Through process mining, organisations can improve business processes by utilizing recorded data. Despite advances in the field, a solid statistical foundation is still lacking. This article contributes statistical tests and measures for treating process behavior as a variable, providing a more objective assessment method.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Mathematics, Interdisciplinary Applications
Antony Overstall, James McGree
Summary: In this paper, an extended framework is proposed to enhance robustness, ensure computational feasibility, and allow realistic prior specification. An asymptotic approximation to the expected loss under an alternative model is derived, and the properties of different loss functions are established. The framework is demonstrated in various experimental design scenarios.
Correction
Health Care Sciences & Services
Suzanne McDonald, Samuel X. Tan, Shamima Banu, Mieke van Driel, James M. McGree, Geoffrey Mitchell, Jane Nikles
PATIENT-PATIENT CENTERED OUTCOMES RESEARCH
(2022)
Article
Health Care Sciences & Services
Suzanne McDonald, Samuel X. Tan, Shamima Banu, Mieke van Driel, James M. McGree, Geoffrey Mitchell, Jane Nikles
Summary: ME/CFS is a chronic condition with unknown causes, characterized by a variety of disabling symptoms. The heterogeneity of symptom presentation makes management challenging, as treatments may not work for all individuals. This study aims to explore the feasibility and acceptability of using novel patient-centred N-of-1 observational designs to investigate symptom fluctuations and triggers in ME/CFS at the individual level.
PATIENT-PATIENT CENTERED OUTCOMES RESEARCH
(2022)
