A Hierarchical Bayesian Method for Market Positioning in Environmentally Conscious Design

2011 ◽  
Author(s):  
Yuan Zhao ◽  
Jianfeng Xu ◽  
Deborah Thurston
Keyword(s):  
Bayesian Method ◽  
Hierarchical Bayesian ◽  
Market Segments ◽  
Environmentally Conscious ◽  
Individual Level ◽  
Attribute Weights ◽  
Customer Preferences ◽  
Hierarchical Bayesian Method ◽  
The Individual ◽  
Environmentally Conscious Design

Environmentally conscious consumers and environmental protection legislation have been driving manufacturers to design, produce, and dispose products in a more environmentally responsible manner. One of the key issues is how to position environmentally conscious products in the marketplace. Environmentally conscious design eventually needs to make the transition into mainstream design, rather than stay in a high-profile niche application. The assumption that all consumers have the same preferences does not hold in the real marketplace. Heterogeneous customer preferences require analysis of customer choice behavior at the individual level. In addition, individual customer preferences can be clustered into aggregate preferences of different market segments that are latent within the customer base. In this paper, a Hierarchical Bayesian method is presented to integrate individual level preference information, which can be used to help manufacturers measure product attribute weights and identify appropriate market segments in which customers value the environmentally conscious design the most. An automobile design case study is used to demonstrate the proposed approach.

Time domain force localization and reconstruction based on hierarchical Bayesian method

2020 ◽  
Vol 472 ◽  
pp. 115222 ◽  
Author(s):  
Wei Feng ◽  
Qiaofeng Li ◽  
Qiuhai Lu ◽  
Bo Wang ◽  
Chen Li
Keyword(s):  
Time Domain ◽  
Bayesian Method ◽  
Hierarchical Bayesian ◽  
Hierarchical Bayesian Method

Incorporating Heterogeneous Customer Preferences With Dirichlet Process Mixture Model for Product Positioning in Environmentally Conscious Design

2012 ◽  
Author(s):  
Yuan Zhao ◽  
Deborah Thurston
Keyword(s):  
Mixture Model ◽  
Dirichlet Process ◽  
Dirichlet Process Mixture ◽  
Design Strategies ◽  
Product Positioning ◽  
Environmentally Conscious ◽  
Dirichlet Process Mixture Model ◽  
Customer Preferences ◽  
Sustainable Products ◽  
Environmentally Conscious Design

Increased environmental protection legislation forces manufacturers to employ environmentally conscious design and manufacturing methods. In addition, customer preferences for energy efficient and environmentally sustainable products influence manufacturers design strategies. In order to influence customer buying behaviors for environmentally friendly products, manufacturers need to understand customer preferences first. Manufacturers can make optimal design decisions based on inference on customers’ decision making models. It is recognized that consumers are heterogeneous in their response to different attributes for any given type of product or service. In this paper, we proposed a framework for incorporating heterogeneous customer preferences with Dirichlet Process mixture model for product positioning in environmentally conscious design. The uncertainty about the functional form of the customer preference distribution can be expressed by using a nonparametric prior, in which the number of clusters grows without bound as the amount of data grows. An automobile design case study is used here to demonstrate the proposed approach.

scholarly journals Airborne LIDAR-Derived Aboveground Biomass Estimates Using a Hierarchical Bayesian Approach

2019 ◽  
Vol 11 (9) ◽  
pp. 1050
Author(s):  
Mengxi Wang ◽  
Qingwang Liu ◽  
Liyong Fu ◽  
Guangxing Wang ◽  
Xiongqing Zhang
Keyword(s):  
Aboveground Biomass ◽  
Bayesian Model ◽  
Bayesian Method ◽  
Airborne Lidar ◽  
Projection Area ◽  
Lidar Data ◽  
Hierarchical Bayesian ◽  
Hierarchical Bayesian Model ◽  
Hierarchical Bayesian Method ◽  
Airborne Lidar Data

Conventional ground survey data are very accurate, but expensive. Airborne lidar data can reduce the costs and effort required to conduct large-scale forest surveys. It is critical to improve biomass estimation and evaluate carbon stock when we use lidar data. Bayesian methods integrate prior information about unknown parameters, reduce the parameter estimation uncertainty, and improve model performance. This study focused on predicting the independent tree aboveground biomass (AGB) with a hierarchical Bayesian model using airborne LIDAR data and comparing the hierarchical Bayesian model with classical methods (nonlinear mixed effect model, NLME). Firstly, we chose the best diameter at breast height (DBH) model from several widely used models through a hierarchical Bayesian method. Secondly, we used the DBH predictions together with the tree height (LH) and canopy projection area (CPA) derived by airborne lidar as independent variables to develop the AGB model through a hierarchical Bayesian method with parameter priors from the NLME method. We then compared the hierarchical Bayesian method with the NLME method. The results showed that the two methods performed similarly when pooling the data, while for small sample sizes, the Bayesian method was much better than the classical method. The results of this study imply that the Bayesian method has the potential to improve the estimations of both DBH and AGB using LIDAR data, which reduces costs compared with conventional measurements.

A hierarchical Bayesian method to resolve an inverse problem of MEG contaminated with eye movement artifacts

NeuroImage ◽  
2009 ◽  
Vol 45 (2) ◽  
pp. 393-409 ◽  
Author(s):  
Yusuke Fujiwara ◽  
Okito Yamashita ◽  
Dai Kawawaki ◽  
Kenji Doya ◽  
Mitsuo Kawato ◽  
...  
Keyword(s):  
Inverse Problem ◽  
Eye Movement ◽  
Bayesian Method ◽  
Hierarchical Bayesian ◽  
Movement Artifacts ◽  
Hierarchical Bayesian Method

Evaluation of hierarchical Bayesian method through retinotopic brain activities reconstruction from fMRI and MEG signals

NeuroImage ◽  
2008 ◽  
Vol 42 (4) ◽  
pp. 1397-1413 ◽  
Author(s):  
Taku Yoshioka ◽  
Keisuke Toyama ◽  
Mitsuo Kawato ◽  
Okito Yamashita ◽  
Shigeaki Nishina ◽  
...  
Keyword(s):  
Bayesian Method ◽  
Hierarchical Bayesian ◽  
Hierarchical Bayesian Method

scholarly journals Hierarchical Bayesian method for mapping biogeochemical hot spots using induced polarization imaging

2016 ◽  
Vol 52 (1) ◽  
pp. 533-551 ◽  
Author(s):  
Haruko M. Wainwright ◽  
Adrian Flores Orozco ◽  
Matthias Bücker ◽  
Baptiste Dafflon ◽  
Jinsong Chen ◽  
...  
Keyword(s):  
Hot Spots ◽  
Bayesian Method ◽  
Induced Polarization ◽  
Hierarchical Bayesian ◽  
Polarization Imaging ◽  
Hierarchical Bayesian Method
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