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bayesian hierarchical model with MCMC for optical cable flame retardancy tests

EXPERT SYSTEMS WITH APPLICATIONS 2013年第9期40卷 3574-3579页

作者： Kuandykov, Lev Corning Inc Modeling & Simulat Dept Corning Sci Ctr St Petersburg 194021 Russia

We report a model and method for calculating the probability of an optical fiber cable passing a flame retardancy test (FRT). The method uses a bayesian approach that accounts for variations in the experimental conditions between tests. We show that adding a hierarchy to the empirical model shrinks the posterior distributions and discuss the applicability of the hierarchical model to various test environments. The model uses the average smoke parameter as the predictor and can be extended to other test responses such as the peak smoke, flame spread, or toxicity. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： bayesian hierarchical model Flame retardancy BUGS

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bayesian hierarchical model-based prognostics for lithium-ion batteries

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2018年 172卷 25-35页

作者： Mishra, Madhav Martinsson, Jesper Rantatalo, Matti Goebel, Kai Lulea Univ Technol SKF Univ Technol Ctr S-97187 Lulea Sweden Lulea Univ Technol Div Operat & Maintenance Engn S-97187 Lulea Sweden Lulea Univ Technol Div Math Sci S-97187 Lulea Sweden NASA Ames Res Ctr Intelligent Syst Div Moffett Field CA 94035 USA

To optimise operation and maintenance, knowledge of the ability to perform the required functions is vital. The ability is governed by the usage of the system (operational issues) and availability aspects like reliability of different components. This paper proposes a bayesian hierarchical model (BHM)-based prognostics approach applied to Li-ion batteries, where the goal is to analyse and predict the discharge behaviour of such batteries with variable load profiles and variable amounts of available discharge data. The BHM approach enables inferences for both individual batteries and groups of batteries. Estimates of the hierarchical model parameters and the individual battery parameters are presented, and dependencies on load cycles are inferred. A BHM approach where the operational and reliability aspects end of life (EoD) and end of life (EoL) is studied where its shown that predictions of EoD can be made accurately with a variable amount of battery data. Without access to measurements, e.g. predicting a new battery, the predictions are based only on the prior distributions describing the similarity within the group of batteries and their dependency on the load cycle. A discharge cycle dependency can also be identified in the result giving the opportunity to predict the battery reliability. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： bayesian hierarchical model Prognostics End of discharge Lithium-ion battery

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bayesian hierarchical model for Assessment of Climate model Biases 3rd

Bayesian Hierarchical Model for Assessment of Climate Model ...

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3rd bayesian Young Statisticians Meeting (BAYSM)

作者： Arisido, Maeregu Woldeyes Gaetan, Carlo Zanchettin, Davide Rubino, Angelo Ca Foscari Univ Venice Dept Environm Sci Informat & Stat Via Torino 155 I-30172 Venice Italy

ISBN: (纸本)9783319540849;9783319540832

Studies of climate change rely on numerical outputs simulated from Global Climate models coupling the dynamics of ocean and atmosphere (GCMs). GCMs are, however, notoriously affected by substantial systematic errors (biases), whose assessment is essential to assert the accuracy and robustness of simulated climate features. This contribution focuses on constructing a bayesian hierarchical model for the quantification of climate model biases in a multi-model framework. The method combines information from a multi-model ensemble of GCM simulations to provide a unified assessment of the bias. It further individuates different bias components that are characterized as non-stationary spatial fields accounting for spatial dependence. The approach is illustrated based on the case of near-surface air temperature bias over the tropical Atlantic and bordering regions from a multi-model ensemble of historical simulations from the fifth phase of the Coupled model Intercomparison Project.

关键词： bayesian hierarchical model Climate bias CMIP5 Posterior inference Spatial analysis

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bayesian hierarchical model for Combining Two-resolution Metrology Data

Bayesian Hierarchical Model for Combining Two-resolution Met...

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作者： Xia, Haifeng Texas A&M University

学位级别：master

This dissertation presents a bayesian hierarchical model to combine two-resolutionmetrology data for inspecting the geometric quality of manufactured parts. The high-resolution data points are scarce, and thus scatter over the surface being measured,while the low-resolution data are pervasive, but less accurate or less precise. Combining the two datasets could supposedly make a better prediction of the geometricsurface of a manufactured part than using a single dataset. One challenge in combining the metrology datasets is the misalignment which exists between the low- andhigh-resolution data *** dissertation attempts to provide a bayesian hierarchical model that canhandle such misaligned datasets, and includes the following components: (a) a Gaussian process for modeling metrology data at the low-resolution level; (b) a heuristicmatching and alignment method that produces a pool of candidate matches andtransformations between the two datasets; (c) a linkage model, conditioned on agiven match and its associated transformation, that connects a high-resolution datapoint to a set of low-resolution data points in its neighborhood and makes a combinedprediction; and finally (d) bayesian model averaging of the predictive models in (c)over the pool of candidate matches found in (b). This bayesian model averagingprocedure assigns weights to different matches according to how much they supportthe observed data, and then produces the final combined prediction of the surface based on the data of both *** proposed method improves upon the methods of using a single dataset aswell as a combined prediction without addressing the misalignment problem. Thisdissertation demonstrates the improvements over alternative methods using both simulated data and the datasets from a milled sine-wave part, measured by two coordinatemeasuring machines of different resolutions, respectively

关键词： bayesian hierarchical model

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Non-blind image blur removal method based on a bayesian hierarchical model with hyperparameter priors

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OPTIK 2020年第0期204卷 164178-000页

作者： Yang, Haoyuan Su, Xiuqin Wu, Jing Chen, Songmao Chinese Acad Sci Xian Inst Opt & Precis Mech Xian 710119 Shaanxi Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Air Force Engn Univ Xian 710051 Shaanxi Peoples R China

In many image blur removal schemes, a proper point spread function is usually estimated in advance from the blurry image, then the latent image comes out by using existing non-blind techniques. However, some of the techniques suffer from strong artifacts. Therefore, an efficient non-blind method plays an important role in image restoration issues. In most models, image priors act as the regularization terms that hold image details and suppress noises. This paper introduces a new image prior based on a parameterized scaled Gaussian model and a gamma distribution, with hyperparameters based on the statistical properties of tens of thousands of images. Our regularized cost function is then formed via a bayesian hierarchical approach. It consists of a data fidelity term and a series of constraints on image gradients in multiple orientations. The former is used to assure the best approximation of the original image, and the latter is for preserving sharp edges. The optimization problem is solved by an effective tail-recursive algorithm based on the conjugate descent technique. Experimental results show that our model can both deal with simulated data and real scenes. The comparisons show our method outperforms others and achieves promising results.

关键词： Image prior Blur removal bayesian hierarchical model Regularization

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A regional bayesian hierarchical model for flood frequency analysis

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STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 2015年第3期29卷 1019-1036页

作者： Yan, Hongxiang Moradkhani, Hamid Portland State Univ Dept Civil & Environm Engn Portland OR 97201 USA

In this study, we propose a regional bayesian hierarchical model for flood frequency analysis. The bayesian method is an alternative to the traditional regional flood frequency analysis. Instead of relying on the delineation of implicit homogeneous regions, the bayesian hierarchical method describes the spatial dependence in its inner structure. Similar to the classical bayesian hierarchical model, the process layer of our model presents the spatial variability of the parameters by considering different covariates (e.g., drainage area, elevation, precipitation). Beyond the three classical layers (data, process, and prior) of the bayesian hierarchical model, we add a new layer referred to as the "L-moments layer". The L-moments layer uses L-moments theory to select the best-fit probability distribution based on the available data. This new layer can overcome the subjective selection of the distribution based on extreme value theory and determine the distribution from the data instead. By adding this layer, we can combine the merits of regional flood frequency and bayesian methods. A standard process of covariates selection is also proposed in the bayesian hierarchical model. The performance of the bayesian model is assessed by a case study over the Willamette River Basin in the Pacific Northwest, U.S. The uncertainty of different flood percentiles can be quantified from the posterior distributions using the Markov Chain Monte Carlo method. Temporal changes for the 100-year flood percentiles are also examined using a 20- and 30-year moving window method. The calculated shifts in flood risk can aid future water resources planning and management.

关键词： Flood frequency analysis bayesian hierarchical model L-moments Climatic extremes

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Fault diagnosis for underdetermined multistage assembly processes via an enhanced bayesian hierarchical model

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JOURNAL OF MANUFACTURING SYSTEMS 2021年第PartA期58卷 280-290页

作者： Yu, Dewen Guo, Junkang Zhao, Qiangqiang Zhao, Dingtang Hong, Jun Xi An Jiao Tong Univ Sch Mech Engn Xian 710049 Peoples R China Xi An Jiao Tong Univ Key Lab Educ Minist Modern Design & Rotor Bearing Syst Xian 710049 Peoples R China

Previous works have shown that only relying on measurement data is generally insufficient to identify root causes of the dimensional variation for the complex manufacturing system. It is also well known that for underdetermined multistage assembly processes (MAPs), the number of measurements is less than that of process errors so that the traditional methods are not available for the variation source identification. Therefore, there exists a substantial challenge for fault diagnosis of underdetermined MAPs, especially for the case with multiple fault patterns. To tackle this problem, a novel approach that integrates statistical analysis with domain knowledge is proposed in this paper. First, the variation propagation model is employed to reveal the inherent relationship between key control characteristics and key product characteristics, and then the corresponding variance model is constructed to interpret process faults by means of the abnormal variance of process errors. Considering that the probability of less process faults is far higher than that of more process faults in MAPs, the problem of fault diagnosis is further transformed into searching the sparse solution of abnormal variance changes for process faults. Afterwards, based on the non-negative property of the covariance matrix, an enhanced bayesian hierarchical model is developed to favor sparse estimation of the variance for the underdetermined system in MAPs. Finally, experimental results of both the numeric simulation and practical case study demonstrate the proposed diagnosis methodology can effectively identify the process faults for different patterns with system noise.

关键词： Fault diagnosis Underdetermined system Multistage assembly process bayesian hierarchical model Sparse solution Variation propagation

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A bayesian hierarchical model for urban air quality prediction under uncertainty

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ATMOSPHERIC ENVIRONMENT 2008年第36期42卷 8464-8469页

作者： Liu, Yong Guo, Huaicheng Mao, Guozhu Yang, Pingjian Peking Univ Coll Environm Sci & Engn Beijing 100871 Peoples R China Tianjin Univ Sch Environm Sci & Technol Tianjin 300072 Peoples R China

Urban air quality is subject to the increasing pressure of urbanization, and, consequently. the potentia I impact of air quality changes must be addressed. A bayesian hierarchical model was developed in this paper for urban air quality predication. Literature data on three pollutants and four external driving factors in Xiamen City, China, were studied. The air quality model structure and prior distributions of model parameters were determined by multivariate statistical methods, including correlation analysis, classification and regression trees (CART), hierarchical cluster analysis (CA), and discriminant analysis (DA). A multiple linear regression (MLR) equation was proposed to measure the relationship between pollutant concentrations and driving variables;and bayesian hierarchical model was introduced for parameters estimation and uncertainty analysis. model fit between the observed data and the modeled values was demonstrated, with mean and median values and two credible levels (2.5% and 97.5%). The average relative errors between the observed data and the mean values of SO2, NOx, and dust fall were 6.81%, 6.79%, and 3.52%, respectively. (c) 2008 Elsevier Ltd. All rights reserved.

关键词： bayesian hierarchical model Markov Chain Monte Carlo (MCMC) Urban air quality Multiple linear regression (MLR)

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A bayesian hierarchical model for Classification with Selection of Functional Predictors

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BIOMETRICS 2010年第2期66卷 463-473页

作者： Zhu, Hongxiao Vannucci, Marina Cox, Dennis D. Univ Texas MD Anderson Canc Ctr Dept Biostat Houston TX 77230 USA Rice Univ Dept Stat Houston TX 77005 USA

In functional data classification: functional observations are often contaminated by various systematic effects, such as random batch effects caused by device artifacts, or fixed effects caused by sample-related factors. These effects may lead to classification bias and thus should not be neglected. Another issue of concern is the selection of functions when predictors consist of multiple functions, smile of which may be redundant. The above issues arise in a real data application where we use fluorescence spectroscopy to detect cervical precancer. In this article, we propose a bayesian hierarchical model that takes into account random batch effects and selects effective functions among multiple functional predictors. Fixed effects or predictors in nonfunctional form are also included in the model. The dimension of the functional data is reduced through orthonormal basis expansion or functional principal components. For posterior sampling, we use a hybrid Metropolis Hastings/Gibbs sampler. which suffers slow mixing. An evolutionary Monte Carlo algorithm is applied to improve the mixing. Simulation and real data application show that the proposed model provides accurate selection of functional predictors as well as good classification.

关键词： bayesian hierarchical model Evolutionary Monte Carlo Functional data classification Functional predictor selection Fluorescence spectroscopy

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Radio Environment Map Construction Based on Spatial Statistics and bayesian hierarchical model

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IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 2021年第3期7卷 767-779页

作者： Xu, Yi-Qun Zhang, Bangning Ding, Guoru Zhao, Bing Li, Shengnan Guo, Daoxing Army Engn Univ Coll Commun Engn Nanjing 210000 Peoples R China

Constructing an infrastructure for spectrum sensing and spectrum sharing on the cloud is a promising technology. In this paper, we propose an analysis framework based on spectrum data gathered by distributed sensors to construct the radio environment map (REM). To the best of our knowledge, this is the first attempt to leverage the power of bayesian analysis and Markov chain Monte Carlo (MCMC) in REM. Specifically, a three-stage bayesian hierarchical model (BHM) is established to imitate the spectrum data generation process under spatially correlated shadow fading. Parameters of BHM are estimated with the MCMC algorithm from data collected by the sensor network. Then, we address the space-dimension spectrum inference problem with the aim to interpolate the signal strength where there is no sensor node by composition sampling. At each point in the area of interest, the posterior predictive distribution of the receive signal strength can be obtained by kernel density smoother. We make spatial inference under two sensor location modes (square lattice located sensors and randomly located sensors) and two scenarios (with and without the information about the signal source), respectively. Simulation results demonstrate that although the randomly located sensors mode is suitable for parameter estimation, the inference performance is not better than the square lattice located sensors mode. Quantitative analysis of the inference performance confirms that the effectiveness of our data analysis framework is compelling.

关键词： Sensors Bayes methods Correlation Rayleigh channels Wireless sensor networks Sensor phenomena and characterization Propagation losses bayesian hierarchical model cooperative spectrum sensing Markov chain Monte Carlo radio environment map spectrum inference

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