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
Automation & Control Systems
Johan Alenlov, Arnaud Doucet, Fredrik Lindsten
Summary: The pseudo-marginal HMC algorithm proposed in this paper combines the advantages of both HMC and pseudo-marginal schemes by controlling the precision parameter N to approximate the likelihood and efficiently sample the marginal posterior of parameters in high-dimensional scenarios. Results from experiments show that the PM-HMC algorithm can significantly outperform standard HMC and pseudo-marginal MH schemes.
JOURNAL OF MACHINE LEARNING RESEARCH
(2021)
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
Statistics & Probability
Lewis J. Rendell, Adam M. Johansen, Anthony Lee, Nick Whiteley
Summary: In order to conduct Bayesian inference with large datasets, it is beneficial to distribute the data across multiple machines. By introducing an instrumental hierarchical model and using an SMC sampler with a sequence of association strengths, approximations of posterior expectations can be improved and the association strength can be adjusted accordingly.
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
(2021)
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
Ecology
Luiza Guimaraes Fabreti, Sebastian Hoehna
Summary: This study explores different methods for assessing convergence in phylogenetics, including deriving a threshold for minimum effective sample size and converting tree samples into traces of absence/presence of splits for standard ESS computation. The Kolmogorov-Smirnov test is suggested for assessing convergence in distribution between replicated MCMC runs, while potential scale reduction factor is deemed biased for skewed posterior distributions. Additionally, the study introduces a method for computing distribution of differences in split frequencies, highlighting the importance of using the 95% quantile for checking convergence in split frequencies.
METHODS IN ECOLOGY AND EVOLUTION
(2022)
Review
Computer Science, Interdisciplinary Applications
Patrick Blonigan, Jaideep Ray, Cosmin Safta
Summary: A simple Bayesian method is presented for inferring and forecasting multiwave outbreaks of COVID-19, using timely epidemiological data to provide short-term forecasts for medical resource planning. The method postulates one- and multiwave infection models, estimates parameters with Markov chain Monte Carlo sampling, and selects between competing disease models using information-theoretic criteria. Demonstrated on COVID-19 outbreaks in California, New Mexico, and Florida, the method is robust to noise, provides useful forecasts with uncertainty bounds, and reliably detects transitions from single-wave to successive surge outbreaks.
ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING
(2021)
Article
Engineering, Civil
Jia-Hua Yang, Heung-Fai Lam, Yong-Hui An
Summary: The paper proposes a new two-phase adaptive MCMC method to address the problem of determining the posterior probability density function (PDF) in Bayesian model updating. By using a parameter-space search algorithm and a weighted MCMC algorithm, samples in the regions of high probability can be generated adaptively without going through computationally demanding multiple levels.
ENGINEERING STRUCTURES
(2022)
Article
Engineering, Electrical & Electronic
Fernando Llorente, Luca Martino, Jesse Read, David Delgado-Gomez
Summary: In this work, we analyze the noisy importance sampling method used in signal processing and machine learning, where target density evaluations are noisy. We introduce a general framework and derive optimal proposal densities that account for the noise variance. The optimal proposals select points in regions with higher noise power, improving the efficiency of the estimation.
Article
Environmental Sciences
Babak Jamhiri, Yongfu Xu, Fazal E. Jalal
Summary: This study investigated different cracking prediction models and performed sensitivity analysis to evaluate the uncertainties of the models and parameters. The findings suggest that the linear elastoplastic model provides reasonable predictions, while soil parameter variations play an important role. Furthermore, the findings of this study can improve the decision-making processes for expansive soil stabilization by considering a variety of environmental conditional probabilities.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2022)
Article
Automation & Control Systems
Maxime Vono, Daniel Paulin, Arnaud Doucet
Summary: This paper investigates the computational challenges of exact Bayesian inference for complex models and proposes a split Gibbs sampler algorithm as an alternative approach. The theoretical analysis, supported by numerical illustrations, suggests that this algorithm performs well in high-dimensional scenarios.
JOURNAL OF MACHINE LEARNING RESEARCH
(2022)
Article
Engineering, Manufacturing
P. Honarmandi, R. Seede, L. Xue, D. Shoukr, P. Morcos, B. Zhang, C. Zhang, A. Elwany, I. Karaman, R. Arroyave
Summary: The Eagar-Tsai (E-T) model in the context of 3D printing was studied systematically from an uncertainty quantification/propagation (UQ/UP) perspective. Model parameters were calibrated against experimental data using Markov Chain Monte Carlo (MCMC) sampling, and posterior distributions of parameter values were propagated. It was found that discrepancies between predicted and measured melt pool depths existed under keyholing conditions, but a physics-based correction improved agreement with experiments without increasing model complexity significantly.
ADDITIVE MANUFACTURING
(2021)
Article
Mathematics, Applied
Nikolaj T. Mucke, Benjamin Sanderse, Sander M. Bohte, Cornelis W. Oosterlee
Summary: In the context of solving inverse problems in physics using Bayesian inference, a new approach called Markov Chain Generative Adversarial Neural Network (MCGAN) is proposed to reduce computational costs. By training a GAN to sample from a low-dimensional latent space and incorporating it into a Markov Chain Monte Carlo method, efficient sampling from the posterior distribution is achieved, replacing the need for high-dimensional priors and expensive forward mappings. The proposed methodology converges to the true posterior in Wasserstein-1 distance and sampling from the latent space is weakly equivalent to sampling in the high-dimensional space.
COMPUTERS & MATHEMATICS WITH APPLICATIONS
(2023)
Article
Statistics & Probability
Christophe Andrieu, Anthony Lee, Sam Power, Andi Q. Wang
Summary: In this study, we investigate the use of weak Poincare inequalities to bound convergence of Markov chains to equilibrium. We show that this approach allows for straightforward derivation of subgeometric convergence bounds for methods such as Independent Metropolis-Hastings sampler and pseudo-marginal methods. The associated proofs are simpler than those relying on drift/minorisation conditions and the tools developed allow us to recover and extend known results.
ANNALS OF STATISTICS
(2022)
Article
Computer Science, Interdisciplinary Applications
Riko Kelter
Summary: This paper introduces a R package that performs Bayesian inference in ANOVA, focusing on effect size estimation instead of hypothesis testing with full posterior inference implemented via MCMC.
Article
Dermatology
Lie Choi, Alex Cook, Crystal Phuan, Aaron Martin, Sam Yang, Derrick Aw, Nisha Suyien Chandran
Summary: In this study, patients using lower doses of ciclosporin showed slower but well-tolerated response, with minimal renal impairment. The greatest control of disease was observed at 6 months.
JOURNAL OF DERMATOLOGICAL TREATMENT
(2021)
Article
Biodiversity Conservation
Sarah Heinrich, Joshua Ross, Thomas N. E. Gray, Steven Delean, Nick Marx, Phillip Cassey
BIOLOGICAL CONSERVATION
(2020)
Article
Biology
Robert C. Cope, Joshua Ross
JOURNAL OF THEORETICAL BIOLOGY
(2020)
Article
Medicine, General & Internal
Rachael Pung, Calvin J. Chiew, Barnaby E. Young, Sarah Chin, Mark I-C Chen, Hannah E. Clapham, Alex R. Cook, Sebastian Maurer-Stroh, Matthias P. H. S. Toh, Cuiqin Poh, Mabel Low, Joshua Lum, Valerie T. J. Koh, Tze M. Mak, Lin Cui, Raymond V. T. P. Lin, Derrick Heng, Yee-Sin Leo, David C. Lye, Vernon J. M. Lee
Letter
Medicine, General & Internal
Oon-Tek Ng, Kalisvar Marimuthu, Po-Ying Chia, Vanessa Koh, Calvin J. Chiew, Liang De Wang, Barnaby E. Young, Monica Chan, Shawn Vasoo, Li-Min Ling, David C. Lye, Kai-qian Kam, Koh-Cheng Thoon
NEW ENGLAND JOURNAL OF MEDICINE
(2020)
Review
Medicine, General & Internal
Minah Park, Alex R. Cook, Jue Tao Lim, Yinxiaohe Sun, Borame L. Dickens
JOURNAL OF CLINICAL MEDICINE
(2020)
Article
Endocrinology & Metabolism
Ken Wei Tan, Borame Sue Lee Dickens, Alex R. Cook
BMJ OPEN DIABETES RESEARCH & CARE
(2020)
Letter
Immunology
Borame Sue Lee Dickens, Jue Tao Lim, Jere Wenn Low, Chun Kiat Lee, Yinxiaohe Sun, Haziq Bin Mohamad Nasir, Farheen Akram Bte Mohamed Akramullah, Gabriel Yan, Jolene Oon, Benedict Yan, Louisa Sun, Alex R. Cook, Paul Anantharajah Tambyah, Louis Yi Ann Chai
CLINICAL INFECTIOUS DISEASES
(2021)
Article
Infectious Diseases
Dennis Liu, Lewis Mitchell, Robert C. Cope, Sandra J. Carlson, Joshua Ross
Article
Infectious Diseases
Mingmei Teo, Nigel Bean, Joshua Ross
Summary: The study suggests that distributing all vaccines prophylactically is generally optimal in controlling the spread of infectious diseases. For small populations, a method for determining the optimal prophylactic allocation is provided. As population size increases, an approximation method for determining an approximately optimal vaccination scheme is detailed, which is consistently at least as good as three strategies reported in the literature across a wide range of parameter values.
Article
Infectious Diseases
Freya M. Shearer, Robert Moss, David J. Price, Alexander E. Zarebski, Peter G. Ballard, Jodie McVernon, Joshua Ross, James M. McCaw
Summary: Global influenza pandemic plans have evolved substantially in recent years with the integration of new scientific research, yet there is still untapped potential for real-time analytics in epidemic decision-making. While pandemic plans recognize the importance of situational awareness and special pandemic studies, there is a need to further explore how information from these activities can be integrated into decision-making processes.
Article
Biochemical Research Methods
Kiesha Prem, Kevin van Zandvoort, Petra Klepac, Rosalind M. Eggo, Nicholas G. Davies, Alex R. Cook, Mark Jit
Summary: Mathematical models have been crucial in understanding the spread of directly-transmissible infectious diseases like COVID-19 and evaluating public health responses. Contact matrices are used to characterize the spread of infectious pathogens, with synthetic matrices being constructed in lieu of representative empirical contact studies. These synthetic matrices have shown qualitative similarities to empirically-constructed contact patterns, allowing for modeling in settings without direct contact data.
PLOS COMPUTATIONAL BIOLOGY
(2021)
Article
Multidisciplinary Sciences
Oliver C. Stringham, Stephanie Moncayo, Eilish Thomas, Sarah Heinrich, Adam Toomes, Jacob Maher, Katherine G. W. Hill, Lewis Mitchell, Joshua V. Ross, Chris R. Shepherd, Phillip Cassey
Summary: The study provides a dataset for conducting large-scale searches for illegally traded wildlife on the Internet by compiling seized taxa along with their intended usage. The dataset can also be filtered for more targeted searches of specific taxa or derived products.
Article
Health Care Sciences & Services
Rayner Kay Jin Tan, Wee Ling Koh, Daniel Le, Sumita Banerjee, Martin Tze-Wei Chio, Roy Kum Wah Chan, Christina Misa Wong, Bee Choo Tai, Mee Lian Wong, Alex R. Cook, Mark I-Cheng Chen, Chen Seong Wong
Summary: This study evaluates the effectiveness of a web drama video series in promoting HIV and other STI testing behaviors among GBMSM. The results show that the intervention has positively impacted participants' intention to test and their regular testing behaviors for HIV and chlamydia/gonorrhea. This intervention has the potential to reach GBMSM who may not have access to conventional prevention messaging.
JOURNAL OF MEDICAL INTERNET RESEARCH
(2022)
Article
Biochemical Research Methods
Haoyang Sun, Alex Perkins, Joel Koo, Borame L. Dickens, Hannah E. Clapham, Alex R. Cook
Summary: This study used an individual-based model to simulate the effectiveness of sustained vector control in different dengue transmission settings. The results identified critical factors influencing the time-varying effectiveness of vector control and can inform future studies and predictions of dengue vector control.
PLOS COMPUTATIONAL BIOLOGY
(2022)
Article
Computer Science, Interdisciplinary Applications
Blair Robertson, Chris Price
Summary: Spatial sampling designs are crucial for accurate estimation of population parameters. This study proposes a new design method that generates samples with good spatial spread and performs favorably compared to existing designs.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Hiroya Yamazoe, Kanta Naito
Summary: This paper focuses on the simultaneous confidence region of a one-dimensional curve embedded in multi-dimensional space. An estimator of the curve is obtained through local linear regression on each variable in multi-dimensional data. A method to construct a simultaneous confidence region based on this estimator is proposed, and theoretical results for the estimator and the region are developed. The effectiveness of the region is demonstrated through simulation studies and applications to artificial and real datasets.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Cheng Peng, Drew P. Kouri, Stan Uryasev
Summary: This paper introduces a novel optimal experimental design method for quantifying the distribution tails of uncertain system responses. The method minimizes the variance or conditional value-at-risk of the upper bound of the predicted quantile, and estimates the data uncertainty using quantile regression. The optimal design problems are solved as linear programming problems, making the proposed methods efficient even for large datasets.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Xiaofei Wu, Hao Ming, Zhimin Zhang, Zhenyu Cui
Summary: This paper proposes a model that combines quantile regression and fused LASSO penalty, and introduces an iterative algorithm based on ADMM to solve high-dimensional datasets. The paper proves the global convergence and comparable convergence rates of the algorithm, and analyzes the theoretical properties of the model. Numerical experimental results support the superior performance of the model.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Xin He, Xiaojun Mao, Zhonglei Wang
Summary: This paper proposes a nonparametric imputation method with sparsity to estimate the finite population mean, using an efficient kernel method and sparse learning for estimation. An augmented inverse probability weighting framework is adopted to achieve a central limit theorem for the proposed estimator under regularity conditions.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Christian H. Weiss, Fukang Zhu
Summary: This study introduces a multiplicative error model (CMEMs) for discrete-valued count time series, which is closely related to the integer-valued generalized autoregressive conditional heteroscedasticity (INGARCH) models. It derives the stochastic properties and estimation approaches of different types of INGARCH-CMEMs, and demonstrates their performance and application through simulations and real-world data examples.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Ming-Hung Kao, Ping-Han Huang
Summary: Optimal designs for sparse functional data under the functional empirical component (FEC) settings are investigated. New computational methods and theoretical results are developed to efficiently obtain optimal exact and approximate designs. A hybrid exact-approximate design approach is proposed and demonstrated to be efficient through simulation studies and a real example.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Mateus Maia, Keefe Murphy, Andrew C. Parnell
Summary: The Bayesian additive regression trees (BART) model is a powerful ensemble method for regression tasks, but its lack of smoothness and explicit covariance structure can limit its performance. The Gaussian processes Bayesian additive regression trees (GP-BART) model addresses this limitation by incorporating Gaussian process priors, resulting in superior performance in various scenarios.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Xichen Mou, Dewei Wang
Summary: Human biomonitoring is a method of monitoring human health by measuring the accumulation of harmful chemicals in the body. To reduce the high cost of chemical analysis, researchers have adopted a cost-effective approach that combines specimens and analyzes the concentration of toxic substances in the pooled samples. To effectively interpret these aggregated measurements, a new regression framework is proposed by extending the additive partially linear model (APLM). The APLM is versatile in capturing the complex association between outcomes and covariates, making it valuable in assessing the complex interplay between chemical bioaccumulation and potential risk factors.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Lili Yu, Yichuan Zhao
Summary: The classical accelerated failure time model is a linear model commonly used for right censored survival data, but it cannot handle heteroscedastic survival data. This paper proposes a Laplace approximated quasi-likelihood method with a continuous estimating equation to address this issue, and provides estimation bias and confidence interval estimation formulas.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Shaobo Jin, Youngjo Lee
Summary: Hierarchical generalized linear models are widely used for fitting random effects models, but the standard error estimators receive less attention. Current standard error estimation methods are not necessarily accurate, and a sandwich estimator is proposed to improve the accuracy of standard error estimation.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Rebeca Pelaez, Ingrid Van Keilegom, Ricardo Cao, Juan M. Vilar
Summary: This article proposes an estimator for the probability of default (PD) in credit risk, derived from a nonparametric conditional survival function estimator based on cure models. The asymptotic expressions for bias, variance, and normality of the estimator are presented. Through simulation and empirical studies, the performance and practical behavior of the nonparametric estimator are compared with other methods.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
L. M. Andre, J. L. Wadsworth, A. O'Hagan
Summary: This paper proposes a dependence model that captures the entire data range in multi-variable cases. By blending two copulas with different characteristics and using a dynamic weighting function for smooth transition, the model is able to flexibly capture various dependence structures.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Niwen Zhou, Xu Guo, Lixing Zhu
Summary: The paper investigates hypothesis testing regarding the potential additional contributions of other covariates to the structural function, given the known covariates. The proposed distance-based test, based on Neyman's orthogonality condition, effectively detects local alternatives and is robust to the influence of nuisance functions. Numerical studies and real data analysis demonstrate the importance of this test in exploring covariates associated with AIDS treatment effects.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Blake Moya, Stephen G. Walker
Summary: A full posterior analysis method for nonparametric mixture models using Gibbs-type prior distributions, including the well known Dirichlet process mixture (DPM) model, is presented. The method removes the random mixing distribution and enables a simple-to-implement Markov chain Monte Carlo (MCMC) algorithm. The removal procedure reduces some of the posterior uncertainty and introduces a novel replacement approach. The method only requires the probabilities of a new or an old value associated with the corresponding Gibbs-type exchangeable sequence, without the need for explicit representations of the prior or posterior distributions. This allows the implementation of mixture models with full posterior uncertainty, including one introduced by Gnedin. The paper also provides numerous illustrations and introduces an R-package called CopRe that implements the methodology.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)