Article
Statistics & Probability
Maxime Vono, Nicolas Dobigeon, Pierre Chainais
Summary: Data augmentation is a ubiquitous technique that improves convergence properties by introducing auxiliary variables. This article introduces a unified framework called AXDA, which can effectively study data augmentation models and optimize inference algorithms.
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
(2021)
Article
Mathematics, Applied
Francisco Louzada, Diego Carvalho do Nascimento, Osafu Augustine Egbon
Summary: Spatial documentation is increasing exponentially due to Big Data in the Internet of Things, but Bayesian spatial statistics are not as well explored compared to other machine-learning models. This systematic review aims to address this gap and identify research opportunities in the past 20 years.
Article
History & Philosophy Of Science
Felipe Romero, Jan Sprenger
Summary: The study examines the advantages of replacing NHST with Bayesian inference in the meta-analytic aggregation of effect sizes, particularly in the presence of publication bias and methodological imperfections. While the move to Bayesian statistics may not solve the replication crisis on its own, it would help eliminate important sources of effect size overestimation in certain conditions.
Article
Astronomy & Astrophysics
Marta Colleoni, Maite Mateu-Lucena, Hector Estelles, Cecilio Garcia-Quiros, David Keitel, Geraint Pratten, Antoni Ramos-Buades, Sascha Husa
Summary: In this study, the authors reanalyze the gravitational-wave event GW190412 using state-of-the-art phenomenological waveform models, focusing on the contribution from subdominant harmonics. They compare the PhenomX and PhenomT waveform models, discussing their construction techniques, computational efficiency, and agreement with other waveform models. Additionally, practical aspects of Bayesian inference, such as run convergence and computational cost, are also discussed.
Article
Computer Science, Theory & Methods
Danilo Alvares, Valeria Leiva-Yamaguchi
Summary: Several joint models for longitudinal and survival data have been proposed, but they often suffer from computational and convergence problems. In this study, we propose a novel two-stage approach that overcomes these issues by using estimations from the longitudinal submodel to specify an informative prior distribution for the random effects in the survival submodel. We compare our approach with other methods using simulation studies and real applications, and the results show that our estimator is more accurate and computationally efficient.
STATISTICS AND COMPUTING
(2023)
Article
Behavioral Sciences
Ladan Shams, Ulrik Beierholm
Summary: The theory of Bayesian causal inference is a powerful and versatile theory that can explain human behavior and brain function, making it highly significant in neuroscience research.
NEUROSCIENCE AND BIOBEHAVIORAL REVIEWS
(2022)
Article
Physics, Fluids & Plasmas
Kai Shimagaki, John P. Barton
Summary: This article proposes a framework for accurately estimating time-integrated quantities using Bezier interpolation and applies it to two dynamical inference problems. The results show that Bezier interpolation reduces estimation bias, especially for data sets with limited time resolution.
Article
Ecology
Johannes Oberpriller, David R. Cameron, Michael C. Dietze, Florian Hartig
Summary: Ecologists rely on complex computer simulations to forecast ecological systems, but uncertainties in model parameters and structure can lead to bias and underestimation. The article proposes a framework for robust inference and suggests solutions such as data rebalancing and bias corrections to improve model accuracy. Developing better methods for robust inference in complex computer simulations is crucial for generating reliable predictions of ecosystem responses.
Article
Mathematics
Francisco Javier Diez, Manuel Arias, Jorge Perez-Martin, Manuel Luque
Summary: OpenMarkov is an open-source software tool designed for probabilistic graphical models, primarily in medicine but also used in other fields and education in over 30 countries. This paper explains how OpenMarkov can be used as a pedagogical tool to teach the main concepts of Bayesian networks and influence diagrams, as well as various inference algorithms.
Article
Engineering, Civil
D. Rossat, J. Baroth, M. Briffaut, F. Dufour, A. Monteil, B. Masson, S. Michel-Ponnelle
Summary: Drying and creep are major factors affecting the continuous strain evolution in aging large prestressed concrete structures. Accurate assessment of long-term strain levels is crucial for evaluating the integrity and safety of these structures. This paper proposes a Bayesian inference methodology for updating uncertain parameters of computational models for large concrete structures. The methodology combines the PC-PCE surrogate modeling technique with the BUS framework to efficiently draw samples from posterior distributions at a reduced computational cost. The proposed approach considers model uncertainties and biases, enabling correction of strain predictions through Bayesian inference. Results obtained by applying the proposed methodology to a mock-up of a Nuclear Containment Building demonstrate its effectiveness in Bayesian updating and bias identification for strain predictions.
ENGINEERING STRUCTURES
(2023)
Article
Engineering, Civil
Francisco Garrido-Valenzuela, Sebastian Raveau, Juan C. Herrera
Summary: The proposed methodology uses wireless sensors to detect vehicles equipped with Bluetooth or Wi-Fi and infers the most likely route by Bayesian inference. Testing showed over 90% prediction performance, especially in scenarios with low sensory density and reduced data availability.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Biochemical Research Methods
Sebastian Persson, Niek Welkenhuysen, Sviatlana M. Shashkova, Samuel R. Wiqvist, Patrick M. Reith, Gregor R. Schmidt, Umberto M. Picchini, Marija R. Cvijovic
Summary: Understanding the causes and consequences of heterogeneity in cellular populations is crucial for disease treatment and population manipulation. In this study, we propose a Bayesian inference framework to elucidate sources of cell-to-cell variability in yeast signaling, providing deeper insights into the causes and consequences of heterogeneity.
PLOS COMPUTATIONAL BIOLOGY
(2022)
Article
Economics
Filipe Rodrigues
Summary: This study proposes an amortized variational inference approach that utilizes stochastic backpropagation, automatic differentiation, and GPU-accelerated computation to enable Bayesian inference in mixed multinomial logit models on large datasets. Furthermore, it demonstrates how normalizing flows can enhance the flexibility of variational posterior approximations. Simulation and real data analysis show that this approach achieves significant computational speedups without compromising estimation accuracy on large datasets.
TRANSPORTATION RESEARCH PART B-METHODOLOGICAL
(2022)
Article
Biology
Jan Boelts, Jan-Matthis Lueckmann, Richard Gao, Jakob H. Macke
Summary: Inferring parameters of computational models is crucial in cognitive neuroscience. Simulation-based inference (SBI) using neural density estimators provides a more efficient way to capture decision-making data. Compared to traditional methods, this approach demonstrates higher accuracy and training efficiency.
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
Biochemistry & Molecular Biology
Josip Ivica, Remigijus Lape, Vid Jazbec, Jie Yu, Hongtao Zhu, Eric Gouaux, Matthew G. Gold, Lucia G. Sivilotti
Summary: This study found that shortening the intracellular domain (ICD) in glycine receptors leads to higher open probabilities and increased efficacy of agonists, indicating a new regulatory role for ICD in pentameric ligand-gated channels.
JOURNAL OF BIOLOGICAL CHEMISTRY
(2021)
Article
Engineering, Multidisciplinary
Sin-Chi Kuok, Ka-Veng Yuen, Stephen Roberts, Mark A. Girolami
Summary: This article introduces a novel propagative broad learning approach for nonparametric modeling of structural health indicators affected by ambient conditions, addressing the challenges of formulating appropriate parametric expression for the relationship between operating conditions and health indicators, as well as efficiently handling growing data during long-term monitoring.
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
(2021)
Article
Multidisciplinary Sciences
Connor Duffin, Edward Cripps, Thomas Stemler, Mark Girolami
Summary: The paper introduces a statistical finite element method for analyzing nonlinear, time-dependent phenomena, focusing on nonlinear internal waves (solitons). A Bayesian approach is used to convert the statistical problem into a nonlinear Gaussian state-space model, suitable for various science and engineering applications. The method is demonstrated with the Korteweg-de Vries equation for solitons, along with algorithms based on extended and ensemble Kalman filters, showing effectiveness through simulation and case studies with experimental data. Examples from additional nonlinear, time-dependent partial differential equations are presented to illustrate the generality of the approach.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2021)
Article
Mathematics, Applied
Toni Karvonen, Chris J. Oates, Mark Girolami
Summary: The paper investigates numerical integration algorithms for functions reproduced by Gaussian kernels, proposing two classes of algorithms and proving their worst-case errors decay exponentially or super-algebrically with the number of evaluations. In contrast to previous work, the algorithms in this paper do not impose constraints on the length-scale parameter of the Gaussian kernel.
MATHEMATICS OF COMPUTATION
(2021)
Article
Pharmacology & Pharmacy
Stephen P. H. Alexander, Alistair Mathie, John A. Peters, Emma L. Veale, Jorg Striessnig, Eamonn Kelly, Jane F. Armstrong, Elena Faccenda, Simon D. Harding, Adam J. Pawson, Christopher Southan, Jamie A. Davies, Richard W. Aldrich, Bernard Attali, Austin M. Baggetta, Elvir Becirovic, Martin Biel, Roslyn M. Bill, William A. Catterall, Alex C. Conner, Paul Davies, Markus Delling, Francesco Di Virgilio, Simonetta Falzoni, Stefanie Fenske, Chandy George, Steve A. N. Goldstein, Stephan Grissmer, Kotdaji Ha, Verena Hammelmann, Israel Hanukoglu, Mike Jarvis, AndersA Jensen, Leonard K. Kaczmarek, Stephan Kellenberger, Charles Kennedy, Brian King, Philip Kitchen, Joseph W. Lynch, Edward Perez-Reyes, Leigh D. Plant, Lachlan Rash, Dejian Ren, Mootaz M. Salman, Lucia G. Sivilotti, Trevor G. Smart, Terrance P. Snutch, Jinbin Tian, James S. Trimmer, Charlotte Van den Eynde, Joris Vriens, Aguan D. Wei, Brenda T. Winn, Heike Wulff, Haoxing Xu, Lixia Yue, Xiaoli Zhang, Michael Zhu
Summary: The Concise Guide to PHARMACOLOGY 2021/22 provides concise overviews of nearly 1900 human drug targets, focusing on selective pharmacology and providing links to an open access knowledgebase. The material presented is substantially reduced compared to the website, offering a permanent, citable record. It covers six major pharmacological targets in a landscape format for easy comparison.
BRITISH JOURNAL OF PHARMACOLOGY
(2021)
Article
Engineering, Multidisciplinary
B. Boys, T. J. Dodwell, M. Hobbs, M. Girolami
Summary: This paper introduces a lightweight, open-source, and high-performance Python package for solving Peridynamics problems in solid mechanics. The solver aims to provide fast analysis tools for a large number of simulations required for 'outer-loop' applications. Significant improvements in execution speed and functionality are demonstrated compared to existing techniques.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2021)
Article
Neurosciences
Josip Ivica, Remigijus Lape, Lucia G. Sivilotti
Summary: Studies demonstrate that acidic pH reduces the sensitivity and efficacy of GlyRs to agonists, diminishing their inhibitory function at neurotransmitter synapses.
JOURNAL OF PHYSIOLOGY-LONDON
(2022)
Article
Engineering, Multidisciplinary
Jan Povala, Ieva Kazlauskaite, Eky Febrianto, Fehmi Cirak, Mark Girolami
Summary: Inverse problems involving partial differential equations (PDEs) are commonly used in science and engineering. While Markov Chain Monte Carlo (MCMC) has been the go-to method for sampling from posterior probability measures, it is computationally infeasible for large-scale problems. Variational Bayes (VB) has emerged as a more computationally tractable alternative, approximating posterior distributions with simpler trial distributions. This work presents a flexible and efficient approach to solving inverse problems using VB methods.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2022)
Article
Computer Science, Interdisciplinary Applications
Connor Duffin, Edward Cripps, Thomas Stemler, Mark Girolami
Summary: Statistical learning methods combined with physically derived mathematical models are gaining attention, particularly the use of Bayesian statistical methodology to incorporate data and address model misspecification. The statFEM approach embeds stochastic forcing within the governing equations and updates the posterior distribution using classical Bayesian filtering techniques. This article introduces a low-rank approximation of the dense covariance matrix to overcome computational scalability challenges and demonstrates its effectiveness in reconstructing sparsely observed data-generating processes of reaction-diffusion problems.
JOURNAL OF COMPUTATIONAL PHYSICS
(2022)
Article
Biology
Josip Ivica, Hongtao Zhu, Remigijus Lape, Eric Gouaux, Lucia G. Sivilotti
Summary: This study identifies a new compound, aminomethanesulfonic acid (AMS), as an efficacious agonist for zebrafish glycine receptors. The analysis of AMS-bound glycine receptors reveals its compact binding pocket and similarity with glycine in terms of channel conformation, shedding light on the determinants of agonist efficacy in pentameric ligand-gated ion channels.
Article
Multidisciplinary Sciences
Thomas Gaskin, Grigorios A. Pavliotis, Mark Girolam
Summary: Computational models are important tools to understand complex systems, but parameter estimation can be challenging. In this study, the authors propose a simple and fast method using neural differential equations to accurately estimate probability densities for model parameters. The method combines multiagent models as forward solvers with a neural network to extract parameters from generated data, enabling quick estimation for large systems.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2023)
Article
Computer Science, Artificial Intelligence
Eky Febrianto, Liam Butler, Mark Girolami, Fehmi Cirak
Summary: This paper demonstrates the application of the statistical finite element method in developing a digital twin of a self-sensing structure. Using captured strain data, the digital twin can predict the true response of a steel railway bridge and generate reasonable predictions at locations without measurement data.
DATA-CENTRIC ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Pranay Seshadri, Andrew B. Duncan, George Thorne, Geoffrey Parks, Raul Vazquez Diaz, Mark Girolami
Summary: This paper presents a Bayesian methodology for interpolating temperature or pressure profiles within an aeroengine. The methodology utilizes spatial Gaussian random fields and Fourier basis for modeling, as well as a novel planar covariance kernel for information transfer between measurement planes. The paper also introduces a sparsity-promoting prior for sparse representations and proposes a Bayesian area average metric for better representation and uncertainty breakdown.
DATA-CENTRIC ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Rebecca Ward, Ruchi Choudhary, Alastair Gregory, Melanie Jans-Singh, Mark Girolami
Summary: The paper introduces a particle filter methodology for continuous calibration of a physics-based model element in a digital twin, applied to an underground farm example. The proposed methodology compares favorably in terms of determination of parameter values distribution and analysis run times, potentially ensuring continuing model fidelity.
DATA-CENTRIC ENGINEERING
(2021)
Article
Computer Science, Interdisciplinary Applications
Steven A. Niederer, Michael S. Sacks, Mark Girolami, Karen Willcox
Summary: Mathematical modeling and simulation are transitioning from development and analysis tools to operational monitoring, control, and decision support, utilizing digital twins that are currently difficult to scale. Challenges and opportunities for scaling digital twins are discussed, along with potential barriers to wider adoption of this technology.
NATURE COMPUTATIONAL SCIENCE
(2021)