.png){kind=icon}
返回
Statistical Agent-Based Models for Discrete Spatio-Temporal Systems
.png){kind=icon}
PRE
AI
DOI：10.1198/jasa.2009.tm09036.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Agent-based models have been used to mimic natural processes in a variety of fields. from biology to social science By specifying mechanistic models that describe how small-scale processes hi net and then scaling them up. agent-based approaches can result in very complicated large-scale behavior while often relying on only a small set of initial conditions and intuitive rules Although many agent-based models are used strictly la a Simulation context. statistical implementations are less common To characterize complex dynamic processes such as the spread of epidemics. we present a hierarchical Bayesian framework for formal statistical agent-based modeling using spatiotemporal binary data Our approach is based on an intuitive parameterization of the system dynamics and Call explicitly accommodate directionally varying dispersal. long distance dispersal. and spatial heterogeneity
Keyword:
Binary data
Cellular automata
Dynamical system
Hierarchical Bayesian model
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
J
IF:
3
论文数:
5.2K
被引数:
4.8W
机构
引用论文
Soil erosion processes. I. The relative importance of rainfall detachment and dunoff entrainment
PREAI
分享收藏
Predicting the spatial distribution of ground flora on large domains using a hierarchical Bayesian model使用分层贝叶斯模型预测大域上地面植物区系的空间分布.png)
LANDSCAPE ECOLOGY
LANDSCAPE ECOLOGYIF3.7
PREAI
分享收藏
查看更多内容