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Inferring personalized visual satisfaction profiles in daylit offices from comparative preferences using a Bayesian approach

机译：使用贝叶斯方法从比较偏好中推断日光办公室的个性化视觉满意度概况

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摘要

This paper presents a new method for developing personalized visual satisfaction profiles in private daylit offices using Bayesian inference. Unlike previous studies based on action data, a set of experiments with human subjects and changing visual conditions were conducted to collect comparative preference data. The likelihood function was defined by linking comparative visual preference data with the visual satisfaction utility function using a probit model structure. A parametrized Gaussian bell function was adopted for the latent satisfaction utility model, based on our belief that each person has a specific set of neighboring visual conditions that are most preferred. Distinct visual preference profiles were inferred with a Bayesian approach using the experimental data. The inferred visual satisfaction utility functions and the model performance results reflect the ability of the models to discover different personalized visual satisfaction profiles. The method presented in this paper will serve as a paradigm for developing personalized preference models, for potential use in personalized controls, balancing human satisfaction with indoor environmental conditions and energy use considerations.
机译：本文提出了一种使用贝叶斯推理在私人采光办公室开发个性化视觉满意度资料的新方法。与以前基于动作数据的研究不同，进行了一系列针对人类受试者和不断变化的视觉条件的实验，以收集比较偏好数据。可能性函数是通过使用概率模型结构将比较视觉偏好数据与视觉满意度实用程序函数链接来定义的。潜在满意度实用程序模型采用了参数化的高斯钟形函数，因为我们相信每个人都有一组特定的邻近视觉条件，这是最可取的。使用实验数据通过贝叶斯方法推断出明显的视觉偏好特征。推断的视觉满意度实用功能和模型性能结果反映了模型发现不同个性化视觉满意度概况的能力。本文介绍的方法将作为开发个性化偏好模型的范例，可在个性化控件中潜在使用，平衡人类满意度与室内环境条件和能源使用注意事项。

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