Probabilistic back analysis method for determining surrounding rock parameters of deep hard rock tunnel by WU Zhong-guang, WU Shun-chuan

“…Last, by combination with the Bayesian (B) analysis method, the B-PSO-MSVM model was established, and surrounding rock parameters were dynamically updated by applying the Markov Chain Monte Carlo simulation algorithm. …”
Get full text

Modelling the effect of bednet coverage on malaria transmission in South Sudan. by Abdulaziz Y A Mukhtar, Justin B Munyakazi, Rachid Ouifki, Allan E Clark

“…Available data on malaria is utilized to determine realistic parameter values of this model using a Bayesian approach via Markov Chain Monte Carlo (MCMC) methods. …”
Get full text

On multicomponent stress–strength reliability for progressively censored logistic exponential model by Amulya Kumar Mahto, Kumar Abhishek, Yogesh Mani Tripathi, Oluwafemi Samson Balogun, Yusra A. Tashkandy, Mahmoud E. Bakr

“…This paper considers reliability estimation for a multicomponent stress–strength (MSS) model under progressively censored data. The classical and Bayesian estimation procedures are employed to evaluate point and interval estimators of the reliability when the failure pattern of the stress and strength components are modeled using the highly flexible logistic exponential distributions with a common shape parameter. …”
Get full text

Statistical Analysis of Inverse Weibull based on Step-Stress Partially Accelerated Life Tests with Unified Hybrid Censoring Data by Amal Soliman Hassan, Abd ELwahab Hagag, Nader Metwally, Omar Hussein Sery

“…For the purpose of estimating the model parameters and acceleration factor, the maximum likelihood approach is applied along with the maximum product of the spacing procedure to generate point and interval estimates. …”
Get full text

TESTING OF CYCLIC STRUCTURAL CHANGES IN SWITCHING REGIME VECTOR AUTOREGRESSIVE MODELS by V. I. Malugin

“…The method is based on a sequential application of two algorithms, realizing the Bayesian “plug-in” decision rule of point wise classification and a statistical test for expected probability of misclassification. …”
Get full text

An accessible method for implementing hierarchical models with spatio-temporal abundance data. by Beth E Ross, Mevin B Hooten, David N Koons

“…Fortunately, new tools have recently been developed that make it more feasible for wildlife biologists to fit sophisticated hierarchical Bayesian models (i.e., Integrated Nested Laplace Approximation, 'INLA'). …”
Get full text

Model-Based Geostatistical Mapping of the Prevalence of Onchocerca volvulus in West Africa. by Simon J O'Hanlon, Hannah C Slater, Robert A Cheke, Boakye A Boatin, Luc E Coffeng, Sébastien D S Pion, Michel Boussinesq, Honorat G M Zouré, Wilma A Stolk, María-Gloria Basáñez

“…<h4>Methods and findings</h4>Pre-control microfilarial prevalence data from 737 villages across the 11 constituent countries in the OCP epidemiological database were used as ground-truth data. These 737 data points, plus a set of statistically selected environmental covariates, were used in a Bayesian model-based geostatistical (B-MBG) approach to generate a continuous surface (at pixel resolution of 5 km x 5km) of microfilarial prevalence in West Africa prior to the commencement of the OCP. …”
Get full text

MODELING LONGITUDINAL MEASUREMENTS OF CLINICAL AND EPIDEMIOLOGICAL PARAMETERS USING JOINPOINT REGRESSION by Mihaela Moscalu, Ionela-Lăcrămioara Șerban, Norin Forna, Mihaela Pundiche, Loredana Mitran, Madalina-Elena Datcu, Nicolae Șerban Dragomir, Alin Constantin Pînzariu

“…This article aims to provide a comprehensive synthesis of the applications of Joinpoint regression in medicine, with a particular focus on the modelling of longitudinal measurements. Materials and methods The Joinpoint regression model was employed in integration with joint longitudinal–time-to-event models, as well as with Bayesian extensions. …”
Get full text

Transforming traffic accident investigations: a virtual-real-fusion framework for intelligent 3D traffic accident reconstruction by Yanzhan Chen, Qian Zhang, Fan Yu

“…While visual rendering by 3D-GS struggles under adverse conditions like nighttime or rain, a clustering parameter stochastic optimization model and mixed-integer programming Bayesian optimization (MIPBO) algorithm are proposed to enhance the segmentation of large-scale 3D point clouds. …”
Get full text

Progressive First-Failure Censoring in Reliability Analysis: Inference for a New Weibull–Pareto Distribution by Rashad M. EL-Sagheer, Mahmoud M. Ramadan

“…Markov chain Monte Carlo sampling is used to obtain Bayesian point estimates and the highest posterior density credible intervals for the parameters and reliability measures. …”
Get full text

Approaching maximum resolution in structured illumination microscopy via accurate noise modeling by Ayush Saurabh, Peter T. Brown, J. Shepard Bryan IV, Zachary R. Fox, Rory Kruithoff, Cristopher Thompson, Comert Kural, Douglas P. Shepherd, Steve Pressé

“…Consequently, achieving high contrast near the maximum theoretical resolution in an unsupervised, physically principled manner remains an open problem. Here, we propose Bayesian-SIM (B-SIM), a Bayesian framework to quantitatively reconstruct SIM data, rectifying these shortcomings by accurately incorporating known noise sources in the spatial domain. …”
Get full text

Large‐Scale Extensional Strain in Southern Tibet From Sentinel‐1 InSAR and GNSS Data by Han Chen, Chunyan Qu, Dezheng Zhao, Xinjian Shan, Chenglong Li, Luca Dal Zilio

Get full text

Generalized nonextensive entropy holographic dark energy models verified by cosmological data by Ilim imdiker, Mariusz P. Da̧browski, Vincenzo Salzano

“…We find the bounds on the specific entropy model parameters and also apply statistical comparison tool such as the Bayesian evidence criterion in order to favour or disfavour the models against standard $$\Lambda $$ Λ CDM. …”
Get full text

Inference for Two-Parameter Birnbaum–Saunders Distribution Based on Type-II Censored Data with Application to the Fatigue Life of Aluminum Coupon Cuts by Omar M. Bdair

“…The Bayesian method employs Markov Chain Monte Carlo (MCMC) sampling for point predictions and credible intervals. …”
Get full text

Probabilistic seasonal dengue forecasting in Vietnam: A modelling study using superensembles. by Felipe J Colón-González, Leonardo Soares Bastos, Barbara Hofmann, Alison Hopkin, Quillon Harpham, Tom Crocker, Rosanna Amato, Iacopo Ferrario, Francesca Moschini, Samuel James, Sajni Malde, Eleanor Ainscoe, Vu Sinh Nam, Dang Quang Tan, Nguyen Duc Khoa, Mark Harrison, Gina Tsarouchi, Darren Lumbroso, Oliver J Brady, Rachel Lowe

“…Bayesian spatiotemporal models were fit to 19 years (2002-2020) of dengue data at the province level across Vietnam. …”
Get full text

A Spatiotemporal Prediction Model for Regional Scheduling of Shared Bicycles Based on the INLA Method by Zhuoran Yu, Yimeng Duan, Shen Zhang, Xin Liu, Kui Li

“…In this study, we make an effort to model a POI-level cycling demand with a Bayesian hierarchical method. …”
Get full text

Surrogate Model for In-Medium Similarity Renormalization Group Method Using Dynamic Mode Decomposition by Sota Yoshida

“…While this is still not an acceleration that is significant enough to enable us to fully quantify, e.g., statistical uncertainties using Bayesian methods, this work offers a starting point for constructing efficient surrogate models for the IMSRG.…”
Get full text

Distribution‐Based Model Evaluation and Diagnostics: Elicitability, Propriety, and Scoring Rules for Hydrograph Functionals by Jasper A. Vrugt

“…This point‐valued mapping necessarily implies a loss of information about model performance. …”
Get full text

Modelling the impact of lockdown-easing measures on cumulative COVID-19 cases and deaths in England by Hisham Ziauddeen, Deepti Gurdasani, Naresh Subramaniam

“…Objectives To assess the potential impacts of successive lockdown-easing measures in England, at a point in the COVID-19 pandemic when community transmission levels were relatively high.Design We developed a Bayesian model to infer incident cases and reproduction number (R) in England, from incident death data. …”
Get full text

Evaluating machine learning models comprehensively for predicting maximum power from photovoltaic systems by Samir A. Hamad, Mohamed A. Ghalib, Amr Munshi, Majid Alotaibi, Mostafa A. Ebied

“…Abstract This paper presents a machine learning (ML) model designed to track the maximum power point of standalone Photovoltaic (PV) systems. …”
Get full text