A Probabilistic Capacity Model and Seismic Vulnerability Analysis of Wall Pier Bridges
Applied Sciences, 2020 This study aims to establish a probabilistic capacity model of a wall pier under various damage states, and the seismic vulnerability of a typical wall pier bridge is studied.
Libo Chen, Yi Tu, Leqia He
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Transition from Swifterbant to Funnelbeaker: A Bayesian Chronological Model
Open Archaeology, 2021 The transition from the late Swifterbant culture to the first appearance of the Funnelbeaker Westgroup raises numerous questions, from cultural discontinuities to gradual transitions.
Menne Julia, Brunner Mirco
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Bayesian Spatial Analysis of the Incidence Rate of Patients with Breast Cancer in Southern Iran [PDF]
Iranian Journal of Medical SciencesBackground: In the female population, breast cancer is the most common cancer and a leading cause of cancer death. This study was designed to investigate the geographical pattern of breast cancer risk in different counties of Fars province in the south ...
Abbas Rezaianzadeh +6 more
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Bayesian optimization for computationally extensive probability distributions [PDF]
, 2018 An efficient method for finding a better maximizer of computationally extensive probability distributions is proposed on the basis of a Bayesian optimization technique.
Hukushima, Koji, Tamura, Ryo
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ModHMM: A Modular Supra-Bayesian Genome Segmentation Method [PDF]
, 2020 Genome segmentation methods are powerful tools to obtain cell type or tissue-specific genome-wide annotations and are frequently used to discover regulatory elements.
Benner, P., Vingron, M.
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EXPERT SYSTEM DESIGN TO DIAGNOSE PESTS AND DISEASES ON LOCAL RED ONION PALU USING BAYESIAN METHOD
Barekeng, 2023 Bayesian is a method that can be used to overcome the uncertainty of a situation or data. The information obtained must be continuously updated so that it can foster trust as a result of the uncertainty of those conditions. In this study, the application
Junaidi Junaidi +5 more
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Efficient Bayesian hierarchical functional data analysis with basis function approximations using Gaussian-Wishart processes [PDF]
, 2016 Functional data are defined as realizations of random functions (mostly smooth functions) varying over a continuum, which are usually collected with measurement errors on discretized grids.
Bonferroni C. E. +8 more
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Bayesian Reconstruction of Missing Observations [PDF]
, 2015 We focus on an interpolation method referred to Bayesian reconstruction in this paper. Whereas in standard interpolation methods missing data are interpolated deterministically, in Bayesian reconstruction, missing data are interpolated probabilistically ...
Kataoka, Shun +2 more
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Frontiers in Psychology, 2020 One practical challenge in observational studies and quasi-experimental designs is selection bias. The issue of selection bias becomes more concerning when data are non-normal and contain missing values.
Dingjing Shi +3 more
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Bayes Estimation of Shape Parameter of Length Biased Weibull Distribution
JTAM (Jurnal Teori dan Aplikasi Matematika), 2021 In this paper, length biased Weibull distribution is considered for Bayesian analysis. The expressions for Bayes estimators of the parameter have been derived under squared error, precautionary, entropy, K-loss, and Al-Bayyati’s loss functions by using ...
Arun Kumar Rao, Himanshu Pandey
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