.png){kind=icon}
返回
Bayesian inference for network-based models with a linear inverse structure
.png){kind=icon}
PRE
AI
DOI：10.1016/j.trb.2010.01.006.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Network-based transport models are used for a host of purposes, from estimation of travel demand through to traffic assignment and traffic flow prediction. Before any such model can be applied in practice we must estimate its parameters and also examine its adequacy. A variety of methods have been developed to do this, but they are typically quite specific to the particular model at hand. In this paper we show that it is possible to develop a unified approach by recognizing that a statistical linear inverse structure arises when attempting to perform inference for any network-based model. This common structure provides the building blocks for the development a coherent theory for Bayesian inference for these models, which has a number of advantages of over more ad hoc methods. While calculation of the Bayesian posterior will typically be intractable, our unified framework allows us to design a generic Markov chain Monte Carlo algorithm for sampling from this distribution in practice. We illustrate the operation of this algorithm on a section of the road network in the English city of Leicester. (c) 2010 Elsevier Ltd. All rights reserved.
Keyword:
Estimation
Markov chain Monte Carlo
Measurement error
Origin-destination matrix
Statistical linear inverse problem
Traffic assignment
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.3
论文数:
3.5K
被引数:
1.9W
机构
暂无机构信息
引用论文
.png){kind=icon}
INFERENCES ON TRIP MATRICES FROM OBSERVATIONS ON LINK VOLUMES - A BAYESIAN STATISTICAL APPROACH
PREAI
分享收藏Crystal chemistry and metal-hydrogen bonding in anisotropic and interstitial hydrides of intermetallics of rare earth (R) and transition metals (T), RT3 and R2T7稀土 (R) 和过渡金属 (T) 的金属间化合物的各向异性和间隙氢化物中的晶体化学和金属氢键，RT3 和R2T7
PREAI
分享收藏
查看更多内容