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hierarchical bayesian modeling for predicting ordinal responses of personalized thermal sensation: Application to outdoor thermal sensation data

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BUILDING AND ENVIRONMENT 2018年 142卷 414-426页

作者： Lim, Jongyeon Akashi, Yasunori Song, Doosam Hwang, Hyokeun Kuwahara, Yasuhiro Yamamura, Shinji Yoshimoto, Naoki Itahashi, Kazuo Univ Tokyo Sch Engn Bunkyo Ku 7-3-1 Hongo Tokyo 1138656 Japan Sungkyunkwan Univ Coll Engn 2066 Seobu Ro Suwon 16419 Gyunggi Do South Korea LG Hausys R&D Ctr 77 Heungan Daero81beon Gil Anyang 14117 Gyunggi Do South Korea MTD Co Ltd Shinagawa Ku 1-2-6 Minamioi Tokyo 1400013 Japan Nikken Sekkei Res Inst Chiyoda Ku 3-7-1 Kanda Ogawamachi Tokyo 1010052 Japan Hitachi Ltd R&D Grp Ctr Exploratory Res Bunkyo Ku 7-3-1 Hongo Tokyo 1138656 Japan Hitachi Res Inst Chiyoda Ku 1-18-13 Sotokanda Tokyo 1018608 Japan

A concept known as 'nudge' has recently received attention in many application domains. It implies influencing the behavior and decision-making of individuals by making indirect suggestions through the presentation of adequate information. We apply such a perspective to improve the value of a space. It can be measured by the number of visitors, and the predicted thermal sensation is considered as information offered to potential visitors. In the present study, we explain how to generate the information required for a successful nudge. This information must be specifically tailored towards personalized characteristics, rather than a one-fits-all approach. This study presents a new data-driven method for predicting individuals' thermal sensation by formulating the effect of both measured (thermal) and non-measured factors on thermal sensation votes. The proposed model is explicitly encoded based on a major premise that "different individuals have different thermal sensation characteristics;however, all individuals also have a common trend." The inference model uses a bayesian approach, and is hierarchically structured to represent dependencies across model parameters of the personalized characteristics of individual-level and the typical trend of group-level thermal sensations. The Markov chain Monte Carlo approach is used to approximate the posterior distribution and draw inferences on the model parameters. The results, based on data collected from outdoor spaces, show that the proposed model provides accurate predictions for personalized thermal sensation and improves the efficiency of parameter estimates. Our approach provides fresh insight into statistical models for predicting thermal sensation.

关键词： Thermal sensation hierarchical bayesian modeling Parameter estimation Uncertainty assessment Nudge

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hierarchical bayesian modeling OF OCEAN HEAT CONTENT AND ITS UNCERTAINTY

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ANNALS OF APPLIED STATISTICS 2022年第4期16卷 2603-2625页

作者： Baugh, Samuel Mckinnon, Karen UCLA Dept Stat Los Angeles CA 90095 USA UCLA Inst Environm & Sustainabil Los Angeles CA 90095 USA

The accurate quantification of changes in the heat content of the world's oceans is crucial for our understanding of the effects of increasing green-house gas concentrations. The Argo program, consisting of Lagrangian floats that measure vertical temperature profiles throughout the global ocean, has provided a wealth of data from which to estimate ocean heat content. How-ever, creating a globally consistent statistical model for ocean heat content remains challenging due to the need for a globally valid covariance model that can capture complex nonstationarity. In this paper, we develop a hierar-chical bayesian Gaussian process model that uses kernel convolutions with cylindrical distances to allow for spatial nonstationarity in all model param-eters while using a Vecchia process to remain computationally feasible for large spatial datasets. Our approach can produce valid credible intervals for globally integrated quantities that would not be possible using previous ap-proaches. These advantages are demonstrated through the application of the model to Argo data, yielding credible intervals for the spatially varying trend in ocean heat content that accounts for both the uncertainty induced from in-terpolation and from estimating the mean field and other parameters. Through cross-validation, we show that our model outperforms an out-of-the-box ap-proach as well as other simpler models. The code for performing this analysis is provided as the R package bayesianOHC.

关键词： hierarchical bayesian modeling ocean heat content nonstationary spatial modeling

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Virtual sensing based on hierarchical bayesian modeling framework using a Laplace-based Gibbs sampler

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APPLIED MATHEMATICAL MODELLING 2023年第1期124卷 167-191页

作者： Zhang, Zifan Peng, Chang Wang, Guangjun Ju, Zengye Ma, Long CRRC Qingdao Sifang Co Ltd Qingdao 266111 Peoples R China

This paper presents a bayesian-based approach to realize virtual strain sensing and quantify the uncertainty in these estimates using sparse output-only measurements. This method relies on output-only measurements to identify the modal coordinates of the structure and realizes the virtual sensing according to the modal superposition method. A robust hierarchical bayesian modeling (HBM) framework is developed to fully account for the uncertainties arising from modeling errors and measurement noise. Moreover, the HBM framework has the ability to quantify the uncertainty of parameters in the statistical model by introducing an additional layer of the prior distribution. The Gibbs sampler is adopted to obtain the marginal posterior distributions of the hyper-parameters, with the implementation of Gibbs methods relying on the derived analytical expressions of the full conditional posterior distributions. The Laplace asymptotic approximation simplified the form of the full conditional posterior distributions, thus improving the computational efficiency. Given the hyper-parameters' marginal posterior distribution, the dynamic parameters' marginal posterior distribution can be calculated according to the total probability theorem. Finally, one can propagate the uncertainty to make predictions of quantities of interest (QoI). Three case studies, including two numerical examples and a laboratory model test, are carried out to verify the accuracy and efficiency of the proposed algorithm. The results show that the estimates of the parameters are close to the true values. Besides, the prediction results of QoI also show high accuracy, verifying the effectiveness of the proposed HBM method in virtual sensing.

关键词： Virtual sensing Modal superposition method hierarchical bayesian modeling Laplace asymptotic approximation Gibbs sampler

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Assimilating multi-source uncertainties of a parsimonious conceptual hydrological model using hierarchical bayesian modeling

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JOURNAL OF HYDROLOGY 2010年第3-4期394卷 436-446页

作者： Wu, Wei Clark, James S. Vose, James M. Duke Univ Nicholas Sch Environm Durham NC 27708 USA Duke Univ Dept Biol Durham NC 27708 USA US Forest Serv USDA Coweeta Hydrol Lab Otto NC 28763 USA

hierarchical bayesian (HB) modeling allows for multiple sources of uncertainty by factoring complex relationships into conditional distributions that can be used to draw Inference and make predictions We applied an HB model to estimate the parameters and state variables of a parsimonious hydrological model - GR4J - by coherently assimilating the uncertainties from the model observations and parameters at Coweeta Basin in western North Carolina A state-space model was within the bayesian hierarchical framework to estimate the daily soil moisture levels and their uncertainties Results show that the posteriors of the parameters were updated from and relatively insensitive to priors an indication that they were dominated by the data The uncertainties of the simulated streamflow increased with streamflow Increase By assimilating soil moisture data the model could estimate the maximum capacity of soil moisture accounting storage and predict storm events with higher precision compared to not assimilating soil moisture data This study has shown that hierarchical bayesian model is a useful tool in water resource planning and management by acknowledging stochasticity (C) 2010 Elsevier B V All rights reserved

关键词： hierarchical bayesian modeling Hydrological modeling Soil moisture Streamflow

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A Flexible hierarchical bayesian modeling Technique for Risk Analysis of Major Accidents

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RISK ANALYSIS 2017年第9期37卷 1668-1682页

作者： Yu, Hongyang Khan, Faisal Veitch, Brian Mem Univ Newfoundland Fac Engn & Appl Sci CRISE St John NF Canada Univ Tasmania Australian Maritime Coll Natl Ctr Maritime Engn & Hydrodynam Launceston Tas Australia

Safety analysis of rare events with potentially catastrophic consequences is challenged by data scarcity and uncertainty. Traditional causation-based approaches, such as fault tree and event tree (used to model rare event), suffer from a number of weaknesses. These include the static structure of the event causation, lack of event occurrence data, and need for reliable prior information. In this study, a new hierarchical bayesian modeling based technique is proposed to overcome these drawbacks. The proposed technique can be used as a flexible technique for risk analysis of major accidents. It enables both forward and backward analysis in quantitative reasoning and the treatment of interdependence among the model parameters. Source-to-source variability in data sources is also taken into account through a robust probabilistic safety analysis. The applicability of the proposed technique has been demonstrated through a case study in marine and offshore industry.

关键词： Event tree fault tree hierarchical bayesian modeling major accidents probabilistic risk analysis

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hierarchical bayesian modeling framework for model updating and robust predictions in structural dynamics using modal features

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2022年第0期170卷 108784-108784页

作者： Jia, Xinyu Sedehi, Omid Papadimitriou, Costas Katafygiotis, Lambros S. Moaveni, Babak Univ Thessaly Dept Mech Engn Volos Greece Hong Kong Univ Sci & Technol Dept Civil & Environm Engn Hong Kong Peoples R China Tufts Univ Dept Civil & Environm Engn Somerville MA USA

The hierarchical bayesian modeling (HBM) framework has recently been developed to tackle the uncertainty quantification and propagation in structural dynamics inverse problems. This new framework characterizes the ensemble variability of structural parameters observed over multiple datasets together with the estimation uncertainty obtained based on the discrepancy between the measured and model outputs. The present paper expands on this framework, developing it further for model inference based on modal features. It generalizes the HBM framework by considering an additional hyper distribution to characterize the uncertainty of prediction error variances across different datasets. Moreover, asymptotic approximations are integrated into the HBM framework to simplify the computation of the posterior distribution of hyper-parameters, providing insights on different sources of uncertainties and the relation of the estimates of the hyper-parameters with the parameter variability and estimation uncertainties. Conditions are presented under which the approximations are expected to be accurate. Introducing the HBM formulation is beneficial, particularly for the propagation of uncertainty based on both structural and prediction error parameters providing reasonable uncertainty bounds. The posterior uncertainty of the structural and prediction error parameters is propagated to estimate data-informed output quantities of interests, including failure probabilities, which offers robustness to the variability over datasets. The proposed approximations are tested and verified using simulated and experimental examples. The effects of the uncertainty due to dataset variability and the prediction error uncertainty are illustrated in these examples.

关键词： hierarchical bayesian modeling Parameter variability Prediction error uncertainty Model updating Response prediction Modal properties

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How does aging affect recognition-based inference? A hierarchical bayesian modeling approach

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ACTA PSYCHOLOGICA 2015年第0期154卷 77-85页

作者： Horn, Sebastian S. Pachur, Thorsten Mata, Rui Max Planck Inst Human Dev D-14195 Berlin Germany Univ Basel CH-4003 Basel Switzerland

The recognition heuristic (RH) is a simple strategy for probabilistic inference according to which recognized objects are judged to score higher on a criterion than unrecognized objects. In this article, a hierarchical bayesian extension of the multinomial r-model is applied to measure use of the RH on the individual participant level and to re-evaluate differences between younger and older adults' strategy reliance across environments. Further, it is explored how individual r-model parameters relate to alternative measures of the use of recognition and other knowledge, such as adherence rates and indices from signal-detection theory (SDT). Both younger and older adults used the RH substantially more often in an environment with high than low recognition validity, reflecting adaptivity in strategy use across environments. In extension of previous analyses (based on adherence rates), hierarchical modeling revealed that in an environment with low recognition validity, (a) older adults had a stronger tendency than younger adults to rely on the RH and (b) variability in RH use between individuals was larger than in an environment with high recognition validity;variability did not differ between age groups. Further, the r-model parameters correlated moderately with an SDT measure expressing how well people can discriminate cases where the RH leads to a correct vs. incorrect inference;this suggests that the r-model and the SDT measures may offer complementary insights into the use of recognition in decision making. In conclusion, younger and older adults are largely adaptive in their application of the RH, but cognitive aging may be associated with an increased tendency to rely on this strategy. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Aging Recognition memory Heuristics Strategy selection hierarchical bayesian modeling

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Estimating the Trial-by-Trial Learning Curve in Perceptual Learning with hierarchical bayesian modeling

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JOURNAL OF COGNITIVE ENHANCEMENT 2024年第4期8卷 346-363页

作者： Zhao, Yukai Liu, Jiajuan Dosher, Barbara Anne Lu, Zhong-Lin NYU Ctr Neural Sci New York NY 10012 USA Univ Calif Irvine Dept Cognit Sci Irvine CA USA Univ Calif Irvine Inst Math Behav Sci Irvine CA USA NYU Shanghai Div Arts & Sci Shanghai Peoples R China NYU Dept Psychol New York NY 10012 USA NYU ECNU Inst Brain & Cognit Neurosci Shanghai Peoples R China

The learning curve serves as a crucial metric for assessing human performance in perceptual learning. It may encompass various component processes, including general learning, between-session forgetting or consolidation, and within-session rapid relearning and adaptation or deterioration. Typically, empirical learning curves are constructed by aggregating tens or hundreds of trials of data in blocks or sessions. Here, we devised three inference procedures for estimating the trial-by-trial learning curve based on the multi-component functional form identified in (Zhao et al., Journal of Vision 24(5):8, 1-22, 2024): general learning, between-session forgetting, and within-session rapid relearning and adaptation. These procedures include a bayesian inference procedure (BIP) estimating the posterior distribution of parameters for each learner independently, and two hierarchical bayesian models (HBMv and HBMc) computing the joint posterior distribution of parameters and hyperparameters at the population, subject, and test levels. The HBMv and HBMc incorporate variance and covariance hyperparameters, respectively, between and within subjects. We applied these procedures to data from two studies investigating the interaction between feedback and training accuracy in Gabor orientation identification across about 2000 trials spanning six sessions (Liu et al., Journal of Vision 10:29-29, 2010;Liu et al., Vision Research 61:15-24, 2012) and estimated the trial-by-trial learning curves at both the subject and population levels. The HBMc generated best fits to the data and the smallest 68.2% half-width credible interval of the learning curves compared to the BIP and HBMv. The HBMc with the multi-component functional form provides a general framework for trial-by-trial analysis of the component processes in perceptual learning and for predicting the learning curve in unmeasured time points.

关键词： Perceptual learning Learning curve hierarchical bayesian modeling Multi-component processes

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Interpretable hierarchical bayesian modeling of Cell-Type Distributions in COVID-19 Disease 56

Interpretable Hierarchical Bayesian Modeling of Cell-Type Di...

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56th Annual Conference on Information Sciences and Systems (CISS)

作者： Parsons, Sarah Whitener, Nathan P. Bhandari, Sapan Khuri, Natalia Wake Forest Univ Dept Comp Sci Winston Salem NC 27109 USA

ISBN: (纸本)9781665417969

High-throughput sequencing of ribonucleic acid molecules is used increasingly to understand gene expression in organs, tissues, and therapies, at a single-cell level. To facilitate the discovery of the heterogeneity and cell-specific factors of the COVID-19 disease, we use an interpretable computational approach that derives cell mixtures from peripheral blood mononuclear cells of healthy donors, and influenza, asymptomatic, mild and severe COVID-19 patients. Cell mixtures are generated using hierarchical bayesian modeling and are subsequently used as features in the gradient boosting tree classifier. Balanced accuracy of five-fold cross-validation was 68%, significantly higher than expected by random chance. Moreover, 11 out of 19 donors' samples were classified accurately. The main advantage of the mixture-based approach compared to the traditional feature-based classification, is its ability to capture associations between genes as well as between cells.

关键词： COVID-19 extreme gradient boosting tree hierarchical bayesian modeling single-cell gene expression

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Nonlinear model updating through a hierarchical bayesian modeling framework

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COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2022年 392卷 114646-114646页

A new time-domain probabilistic technique based on hierarchical bayesian modeling (HBM) framework is proposed for calibration and uncertainty quantification of hysteretic type nonlinearities of dynamical systems. Specifically, probabilistic hyper models are introduced respectively for material hysteretic model parameters as well as prediction error variance parameters, aiming to consider both the uncertainty of the model parameters as well as the prediction error uncertainty due to unmodeled dynamics. A new asymptotic approximation is developed to simplify the process of nonlinear model updating and substantially reduce the computational burden of the HBM framework. This asymptotic approximation is further employed to provide insightful expressions on the hyper parameters for both the model and prediction error variance parameters. Given a large number of data points within a dataset, the hyper model parameters are formulated to be independent of the hyper parameters for prediction error variance parameter. Two numerical examples are conducted to verify the accuracy and performance of the proposed method considering Bouc-Wen (BW) hysteretic type nonlinearities. Model error is manifested as uncertainty due to variability in the measured data from multiple datasets. Results from a five-story numerical structure indicate that the model error is the main source of error that can affect the uncertainty in the model parameters due to the variability in the experimental data. It is also demonstrated that the parameter uncertainty due to the variability arising from model error depends on the sensor locations. It is shown that the proposed approach is robust for not only quantifying uncertainties of structural parameters and prediction error parameters, but also predicting the system quantities of interests (QoI) with reasonable accuracy and providing reliable uncertainty bounds, as opposed to the conventional bayesian approach which often severely underestimates the uncert

关键词： hierarchical bayesian modeling Nonlinear system Bouc-Wen model Structural parameter uncertainty Prediction error uncertainty Time-domain response hierarchical bayesian modeling Nonlinear system Bouc-Wen model Structural parameter uncertainty Prediction error uncertainty Time-domain response

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