Statistical Modeling统计建模

【课程名称】Statistical Modeling统计建模

【课程级别】本科生

【教学小组】Dr. Dietze; Claire Baldeck

【授课学校】伊利诺伊大学厄巴纳-香槟分校；统计学系

【课程介绍】

Course Description: Researchers in the biological and environmental sciences are often confronted with data that is complex in nature and is beyond the assumptions of classical statistical tests. The goal of confronting our scientific theories with data often requires us embrace the complexity of our data -- to make inference on indirectly observed quantities, to bring multiple types of data to bear on a single question, to synthesize past observations with new data, or to separate the effects of different error processes (e.g. observation error vs. inherent variability in the process). This class provides an introduction to modern statistical modeling from both likelihood and Bayesian perspectives. The focus is on science-driven, problem-specific design of statistical analyses for complex data. Topics include point estimation, interval estimation, model selection, regression, non-linear models, non-Gaussian models & GLMs, hierarchical models, time-series analysis, spatial models, data assimilation, and statistical forecasting. Computational methods such as numerical optimization and Markov-Chain Monte-Carlo simulation are covered with a focus on hands-on application to real data. Course is designed around case-study problem sets using R and BUGS. Advanced graduate students are encouraged to use their own datasets for class projects.

【教学资源】

此课程有 37 个资源,分类如下:

教学大纲1

课程表1

教学课件35

【参考教材】

Models for Ecological Data