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)
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
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
Engineering, Environmental
Marco Bacci, Jonas Sukys, Peter Reichert, Simone Ulzega, Carlo Albert
Summary: Due to limited knowledge about complex environmental systems, predicting their behavior under different scenarios or decision alternatives is uncertain. Considering, quantifying, and communicating this uncertainty is important for societal decisions. Stochastic models are often necessary to adequately describe uncertainty, but calibrating these models presents methodological and numerical challenges. To address this, we compare three numerical approaches and find that their performance is comparable for analyzing a stochastic hydrological model with hydrological data, suggesting that generality and practical considerations can guide technique choice for specific applications.
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
(2023)
Article
Economics
Nianling Wang, Zhusheng Lou
Summary: The stochastic volatility (SV) model is widely used to study time-varying volatility. However, the linearity assumption for transition equation in basic SV model is restrictive. To allow for nonlinearity, we proposed a semiparametric SV model that specifies a nonparametric transition equation for log-volatility using natural cubic splines. The empirical applications to Bitcoin and convertible bond return data indicate that the transition equations of their log-volatility are highly nonlinear. Taking nonlinearity into account, the semi-parametric SV model can improve the likelihood of the basic SV model both in-sample and out-of-sample.
ECONOMIC MODELLING
(2023)
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)
Review
Green & Sustainable Science & Technology
D. Hou, I. G. Hassan, L. Wang
Summary: Building Energy Model (BEM) calibration is crucial for accuracy, with recent focus on stochastic Bayesian inference calibration. However, confusion remains regarding theory, strengths, limitations, and implementations. Selecting appropriate mathematical models and tools poses a challenge.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2021)
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
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
Acoustics
Zhenrui Peng, Zenghui Wang, Hong Yin, Yu Bai, Kangli Dong
Summary: This paper presents a new Bayesian model updating method that addresses the issues of low sampling efficiency and reliance on single-chain proposal distribution in traditional Markov chain Monte Carlo algorithms. The method incorporates delayed rejection and adaptive strategies to obtain multiple Markov chains from different proposal distributions, which can independently adjust proposal distribution variances and improve the acceptance rate of candidate samples. An abnormal chain detection criterion is used to eliminate abnormal Markov chains. The method also utilizes a multi-source sensors grouping weighted fusion algorithm and a Kriging surrogate model to improve updating accuracy and computational efficiency.
JOURNAL OF SOUND AND VIBRATION
(2022)
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)
Article
Engineering, Multidisciplinary
Ke Zhang, Kailun Su, Yunhan Yao, Qingsong Li, Suan Chen
Summary: This paper presents a dynamic evaluation model of Markov chain Monte Carlo (MCMC) roundness error measurement uncertainty based on a stochastic process. The model samples the stochastic process using the MCMC method and calculates the state transition function to reflect the autocorrelation characteristics of the parameters. A comparison between high-precision and low-precision measurements verifies the accuracy and stability of the model, showing that the MCMC method is consistent with the traditional GUM method and Monte Carlo method. The MCMC method based on the stochastic process achieves dynamic evaluation of roundness error measurement uncertainty, obtaining accurate results and improving the evaluation accuracy.
Article
Computer Science, Interdisciplinary Applications
DanHua ShangGuan
Summary: The Monte Carlo method is a powerful tool in many research fields, but the increasing complexity of physical models and mathematical models requires efficient algorithms to overcome the computational cost.
JOURNAL OF COMPUTATIONAL PHYSICS
(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)
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, Electrical & Electronic
Sultan Sial, Aly R. Seadawy, Nauman Raza, Adnan Khan, Ahmad Javid
Summary: Mahavier and Montgomery constructed a Sobolev space for the approximate solution of linear initial value problems using a single-iteration descent method, demonstrating the existence of a best Sobolev gradient for finite difference approximation. They then explored the potential application of single-iteration convergence in an appropriate Sobolev space for a broader class of problems.
OPTICAL AND QUANTUM ELECTRONICS
(2021)
Article
Multidisciplinary Sciences
Adnan Khan, Mohsin Ali, Wizda Iqbal, Mudassar Imran
Summary: The study formulates a deterministic model for COVID-19 transmission and evaluates control strategies, emphasizing the impact of age and co-morbidities on disease severity and mortality. Results show the importance of vaccination and medication for effective disease control, with the proportion of high and low risk populations having a significant effect on disease burden and mortality.
Article
Multidisciplinary Sciences
Danish A. Ahmed, Ali R. Ansari, Mudassar Imran, Kamal Dingle, Michael B. Bonsall
Summary: This study modeled the movement and infection process of host populations, finding that on a short-time scale, population diffusion and movement behavior type do not significantly affect infection levels. The efficacy of lockdown measures depends on the spatial distribution of susceptible and infectious individuals.
Article
Multidisciplinary Sciences
Asgher Ali, Mudassar Imran, Sultan Sial, Adnan Khan
Summary: Mathematical models are useful in determining optimal antibiotic dosing strategies for both susceptible and resistant bacteria. This study proposes two different models of resistance acquisition and uses numerical optimization algorithms to find the best dosing strategy. The optimal dosing strategy depends on the scenario, with different strategies for minimizing total bacterial population and minimizing population at the end of dosing period.
Article
Engineering, Electrical & Electronic
Samad Wali, Chunming Li, Mudassar Imran, Abdul Shakoor, Abdul Basit
Summary: This paper analyzes and tests the efficiency of the alternating direction method of multipliers (ADMM) for level-set based image segmentation. The comparison with the classical gradient descent method shows the effectiveness and efficiency of the ADMM method. Experimental results on medical image segmentation demonstrate an average segmentation coefficient of 0.97 (Dice) and 0.92 (Jaccard), with an average running time of 1.70 seconds and average estimation values of 0.0932 (MAD), 0.993 (accuracy), 0.981 (sensitivity), and 0.964 (specificity).
Article
Mathematical & Computational Biology
Mohsin Ali, Adnan Khan, Shaper Mirza, Mudassar Imran
Summary: In this study, a model for the transmission dynamics of co-infection with influenza and pneumococcal pneumonia is presented, showing the effects of influenza co-infection on pneumonia transmission. It is found that when the reproductive number of influenza is equal to or less than 1 and the reproductive number of pneumonia is equal to or greater than 1, influenza is driven to extinction while pneumonia remains endemic, and vice versa. The existence of a co-infection equilibrium is also demonstrated, which can lead to a backward bifurcation in the system under certain conditions, making the control of the infection more difficult.
INTERNATIONAL JOURNAL OF BIOMATHEMATICS
(2023)
Article
Mathematics, Interdisciplinary Applications
Muhammad Bilal Riaz, Ali Raza Ansari, Adil Jhangeer, Muddassar Imran, Choon Kit Chan
Summary: In this study, we propose a fractional non-linear coupled option pricing and volatility system as an alternative to the Black-Scholes model. We utilize the inverse scattering transformation and phi(6)-expansion algorithm to generate solitonic wave structures and analyze the system's behavior. The graphical representations help predict suitable parameter values that align with the data.
FRACTAL AND FRACTIONAL
(2023)
Article
Mathematics, Applied
Mudassar Imran, Muhammad Usman, Muhammad Dur-e-Ahmad, Adnan Khan
Summary: A deterministic model is proposed to investigate the transmission dynamics of Zika fever, taking into account effects of horizontal and vertical disease transmission. The expression for basic reproductive number R-0 is determined based on transmission rates, and model stability is analyzed, showing local asymptotic stability when R-0 < 1. The study also finds that disease persists strongly when R-0 > 1, with an endemic equilibrium that is locally asymptotically stable.
DIFFERENTIAL EQUATIONS AND DYNAMICAL SYSTEMS
(2021)
Article
Mathematics, Interdisciplinary Applications
Muhammad Usman, Shaaban Abdallah, Mudassar Imran
Summary: This work focuses on studying the response of a ship rolling in regular beam waves, using a one degree of freedom model for nonlinear ship dynamics. The study includes analyzing the effects of various parameters on the stability of steady states and presenting slope stability theorems. The asymptotic perturbation method is used to study primary resonance phenomena and how the variation of bifurcation parameters affects the bending of the bifurcation curve.
MATHEMATICAL AND COMPUTATIONAL APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Muhammad Dur-e-Ahmad, Mudassar Imran
INTERNATIONAL JOURNAL OF INTERACTIVE MULTIMEDIA AND ARTIFICIAL INTELLIGENCE
(2020)
Article
Ecology
Mohsin Ali, Syed Touqeer H. Shah, Mudassar Imran, Adnan Khan
JOURNAL OF BIOLOGICAL DYNAMICS
(2020)
Article
Ecology
Mohsin Ali, Mudassar Imran, Adnan Khan
JOURNAL OF BIOLOGICAL DYNAMICS
(2020)
