Article
Astronomy & Astrophysics
Rossella Gamba, Sebastiano Bernuzzi, Alessandro Nagar
Summary: Interpreting binary neutron star properties from gravitational-wave observations requires generation of millions of waveforms spanning a wide frequency range. Combining effective-one-body waveforms with stationary phase approximation allows for efficient generation of multipolar approximants valid at any frequency range.
Article
Astronomy & Astrophysics
Hector Estelles, Marta Colleoni, Cecilio Garcia-Quiros, Sascha Husa, David Keitel, Maite Mateu-Lucena, Maria de Lluc Planas, Antoni Ramos-Buades
Summary: We present a phenomenological model for gravitational-wave signals emitted by quasicircular precessing binary black holes systems. The model utilizes a time-dependent rotation to map nonprecessing signals to precessing ones and provides a more accurate and computationally efficient computation method in the time domain.
Article
Engineering, Mechanical
Quentin Dollon, Jerome Antoni, Antoine Tahan, Martin Gagnon, Christine Monette
Summary: This paper introduces a fast Gibbs sampler for solving a fully Bayesian problem in operational modal analysis. The sampler is able to infer modal properties from the FFT of well-separated modes and capture system identification and related uncertainties through a posterior distribution. The sampling scheme demonstrates fast convergence through two strategies.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Statistics & Probability
Yves Atchade, Liwei Wang
Summary: This paper proposes a fast approximate Markov chain Monte Carlo sampling framework for a large class of sparse Bayesian inference problems. The computational cost per iteration in several regression models is of order O(n(s+J)), which can be further reduced by data sub-sampling. The algorithm is an extension of the asynchronous Gibbs sampler and can be viewed as a form of Bayesian iterated sure independent screening.
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
(2023)
Article
Agriculture, Dairy & Animal Science
Motohide Nishio, Aisaku Arakawa
Summary: The study demonstrates that the accuracy of variance components and breeding values estimates for a multi-trait animal model using NUTS with the LKJ prior is equal to or higher than those obtained with restricted maximum likelihood or Gibbs sampling. When the population size is small, NUTS with the LKJ prior could be considered as an alternative sampling method for multi-trait analysis in animal breeding.
GENETICS SELECTION EVOLUTION
(2022)
Article
Astronomy & Astrophysics
Vaibhav Tiwari, Charlie Hoy, Stephen Fairhurst, Duncan MacLeod
Summary: VARAHA is an open-source, fast, non-Markovian sampler used for estimating gravitational-wave posteriors. It differs from existing sampling algorithms by discarding low-likelihood regions instead of sampling high-likelihood regions, allowing for significantly faster analyses. VARAHA offers many benefits, such as accurate sky localization and rapid posterior estimation, making it useful for gravitational-wave studies, particularly for binary neutron star observations.
Article
Astronomy & Astrophysics
Kaze W. K. Wong, Maximiliano Isi, Thomas D. P. Edwards
Summary: This paper presents a lightweight, flexible, and high-performance framework for inferring the properties of gravitational-wave events. By employing likelihood heterodyning, automatically differentiable and accelerator-compatible waveforms, and gradient-based Markov Chain Monte Carlo sampling enhanced by normalizing flows, the authors achieve full Bayesian parameter estimation for real events within a minute of sampling time.
ASTROPHYSICAL JOURNAL
(2023)
Article
Mathematics
Luca Martino, Fernando Llorente, Ernesto Curbelo, Javier Lopez-Santiago, Joaquin Miguez
Summary: A novel adaptive importance sampling scheme is proposed for Bayesian inversion problems, where variables of interest and data noise power are inferred using different methods. The technique involves iterative steps of sampling and optimization, with the noise power acting as a tempered parameter for the posterior distribution of the variables of interest. Numerical experiments show the benefits of the proposed approach in Bayesian analysis.
Article
Computer Science, Interdisciplinary Applications
Jiabao Xu, Yu Wang, Lulu Zhang
Summary: A novel data fusion method, collaborative Bayesian compressive sampling (Co-BCS), is proposed to address the challenge of interpolating extremely sparse geo-data. The method integrates correlated secondary data sources and quantifies interpolation uncertainty simultaneously. Results demonstrate that Co-BCS can properly interpret sparse geo-data and quantify associated uncertainty.
COMPUTERS AND GEOTECHNICS
(2021)
Article
Environmental Sciences
Bulent Tutmez
Summary: An uncertainty-based Bayesian strategy was developed and tested among environmental laboratories to address the interlaboratory agreement problem. The proposed hybrid approach showed no sensitivity to outliers and had a transparent and robust agreement structure. The algorithmic procedure can explore both laboratory performances and conformity between independent samples.
SCIENCE OF THE TOTAL ENVIRONMENT
(2023)
Article
Statistics & Probability
Eduardo S. B. de Oliveira, Mario de Castro, Cristian L. Bayes, Jorge L. Bazan
Summary: This study proposes a quantile parametric mixed regression model based on new bounded support distributions, with estimation of parameters using Bayesian approach. The proposed models show good performance in recovering parameters, and they provide an alternative modeling tool for analyzing bounded data.
COMPUTATIONAL STATISTICS
(2022)
Article
Engineering, Multidisciplinary
F. Llorente, E. Curbelo, L. Martino, V. Elvira, D. Delgado
Summary: This study focuses on layered adaptive importance sampling algorithms and proposes different enhancements to increase efficiency and reduce computational costs. Strategies for designing cheaper schemes are also introduced. Numerical experiments demonstrate the advantages of the proposed schemes in handling computational challenges in real-world applications.
APPLIED MATHEMATICAL MODELLING
(2022)
Review
Multidisciplinary Sciences
Arnaud Doucet, Eric Moulines, Achille Thin
Summary: Latent variable models are popular and have been combined with neural networks to create deep latent variable models. However, the intractability of their likelihood function requires approximations for inference. The article reviews recent strategies such as importance sampling, Markov chain Monte Carlo, and sequential Monte Carlo to improve the bounds of the evidence lower bound (ELBO) for these models.
PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
(2023)
Article
Statistics & Probability
Eman Ahmed Alawamy, Yuanyuan Liu, Yiqiang Q. Zhao
Summary: This paper uses the NUTS algorithm to perform Bayesian inference for the traffic intensity of the M/M/1 queue. The results show that the NUTS algorithm outperforms other algorithms.
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
(2022)
Article
Computer Science, Theory & Methods
Lisa Gaedke-Merzhaeuser, Janet van Niekerk, Olaf Schenk, Havard Rue
Summary: This work presents parallelization strategies for the methodology of integrated nested Laplace approximations (INLA) to meet the growing demand of larger-scale Bayesian inference tasks. The introduced approach leverages nested thread-level parallelism, robust regression and state-of-the-art sparse linear solver PARDISO, resulting in significant speedups in various real-world applications. The improved parallelization scheme is already integrated into the open-source R-INLA package for convenient use.
STATISTICS AND COMPUTING
(2023)
Article
Computer Science, Software Engineering
Bradley M. Bell, Kasper Kristensen
OPTIMIZATION METHODS & SOFTWARE
(2018)
Article
Fisheries
Uffe Hogsbro Thygesen, Kasper Kristensen, Teunis Jansen, Jan E. Beyer
ICES JOURNAL OF MARINE SCIENCE
(2019)
Article
Ecology
Mollie E. Brooks, Kasper Kristensen, Maria Rosa Darrigo, Paulo Rubim, Maria Uriarte, Emilio Bruna, Benjamin M. Bolker
Article
Marine & Freshwater Biology
Cole C. Monnahan, Jorge Acevedo, A. Noble Hendrix, Scott Gende, Anelio Aguayo-Lobo, Francisco Martinez
MARINE MAMMAL SCIENCE
(2019)
Article
Fisheries
Cole C. Monnahan, Trevor A. Branch, James T. Thorson, Ian J. Stewart, Cody S. Szuwalski
ICES JOURNAL OF MARINE SCIENCE
(2019)
Article
Food Science & Technology
Sofie Podenphant, Minh H Truong, Kasper Kristensen, Per B. Brockhoff
FOOD QUALITY AND PREFERENCE
(2019)
Article
Biodiversity Conservation
Grete E. Dinesen, Stefan Neuenfeldt, Alexandros Kokkalis, Andreas Lehmann, Josefine Egekvist, Kasper Kristensen, Peter Munk, Karin Hussy, Josianne G. Stottrup
JOURNAL OF COASTAL CONSERVATION
(2019)
Article
Marine & Freshwater Biology
Trevor A. Branch, Cole C. Monnahan
Summary: Deviation from equal sex ratios in blue whales can be explained by faster female growth and size-selective whaling. Despite some fluctuations in sex ratios at different locations and seasons, overall, the sex ratios remain close to equality. Males are slightly more common than females, possibly due to faster female growth and selective whaling practices.
MARINE MAMMAL SCIENCE
(2021)
Article
Multidisciplinary Sciences
Stephanie B. Borrelle, Jeremy Ringma, Kara Lavender Law, Cole C. Monnahan, Laurent Lebreton, Alexis McGivern, Erin Murphy, Jenna Jambeck, George H. Leonard, Michelle A. Hilleary, Marcus Eriksen, Hugh P. Possingham, Hannah De Frond, Leah R. Gerber, Beth Polidoro, Akbar Tahir, Miranda Bernard, Nicholas Mallos, Megan Barnes, Chelsea M. Rochman
Article
Fisheries
Cole C. Monnahan, James T. Thorson, Stan Kotwicki, Nathan Lauffenburger, James N. Ianelli, Andre E. Punt
Summary: Abundance indices from scientific surveys play a crucial role in stock assessment, but variations in fish availability over space and time can lead to inaccuracies. A vertically integrated index that considers spatiotemporal correlation and gear availability is essential for estimating more accurate indices for semi-pelagic fish species like walleye pollock. Understanding the spatial and temporal patterns in vertical distribution and gear availability is important for improving stock assessment methods.
ICES JOURNAL OF MARINE SCIENCE
(2021)
Article
Ecology
Marie-Christine Rufener, Kasper Kristensen, J. Rasmus Nielsen, Francois Bastardie
Summary: This study developed a flexible species distribution model that integrates commercial fisheries and scientific survey data while filtering out their relative bias contributions. The results indicated that the use of commercial fisheries data is suitable for the integrated model, providing additional information on the spatiotemporal abundance dynamics of cod. The integrated model also showed a reduction in uncertainty of predicted abundance fields and fixed-effect parameters.
ECOLOGICAL APPLICATIONS
(2021)
Article
Fisheries
Joaquin Cavieres, Cole C. Monnahan, Aki Vehtari
Summary: Sea urchin is an important benthic resource in Chile, with subpopulations interconnected through larval dispersion. Existing assessment methods overlook spatial dependence among populations, while a newly proposed Bayesian model with explicit spatial dependence shows statistical improvement and consistency with data, leading to better stock sustainability.
FISHERIES RESEARCH
(2021)
Article
Fisheries
Vanessa Trijoulet, Christoffer Moesgaard Albertsen, Kasper Kristensen, Christopher M. Legault, Timothy J. Miller, Anders Nielsen
Summary: This study shows that using Pearson residuals to analyze goodness of the fit, when data are fitted using a multivariate distribution, is incorrect and one-step-ahead (OSA) or forecast quantile residuals should be used instead. The study describes the calculation of OSA residuals specifically to de-correlate compositional observations for the multivariate distributions most commonly used in assessment models.
FISHERIES RESEARCH
(2023)
Article
Fisheries
Alex De Robertis, Mike Levine, Nathan Lauffenburger, Taina Honkalehto, James Ianelli, Cole C. Monnahan, Rick Towler, Darin Jones, Sarah Stienessen, Denise McKelvey
Summary: In 2020, disruptions caused by the COVID-19 pandemic led to the cancellation of fisheries surveys, including the largest fishery in the United States for walleye pollock. To compensate for the lost information, uncrewed surface vehicles equipped with echosounders were deployed to extend the acoustic-trawl time series of pollock abundance. This innovative approach combined historical surveys with data from USVs to provide a consistent fishery-independent index for stock assessment when ship-based surveys were not feasible.
ICES JOURNAL OF MARINE SCIENCE
(2021)
Article
Environmental Studies
Pernille Nielsen, Mette Moller Nielsen, Ciaran McLaverty, Kasper Kristensen, Kerstin Geitner, Jeppe Olsen, Camille Saurel, Jens Kjerulf Petersen
Summary: The article outlines the ecosystem-based management approach and key strategies implemented in Danish N2000 areas for bivalve fisheries, ensuring the sustainability of fisheries and conservation status of designated areas. Strategies include mandatory monitoring systems on fishing vessels, acceptance of a maximum 15% cumulative fishery impact, and high resolution spatial habitat mapping.