GEN III綜三 837 T2
Bayesian data analysis refers to practical inferential methods that use probability models for both observable and unobservable quantities. The flexibility and generality of these methods allow them to address complex real-life problems that are not amenable to other techniques. This course will provide a pragmatic introduction to Bayesian methodology and their powerful applications. Topics include: the basics of Bayesian inference for single and multiparameter models, regression, hierarchical models, model checking, approximation of a posterior distribution by iterative and non- iterative sampling methods, missing data, and Bayesian nonparametrics. Specific topics and the course outline are subject to change as the semester progresses. All topics will be motivated by problems from the physical, life, social, and management sciences. Conceptual understanding and inference via computer simulation will be emphasized throughout the course.
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08:00108:50 | |||||
09:00209:50 | |||||
10:10311:00 | |||||
11:10412:00 | |||||
12:10n13:00 | |||||
13:20514:10 | |||||
14:20615:10 | |||||
15:30716:20 | |||||
16:30817:20 | |||||
17:30918:20 | |||||
18:30a19:20 | |||||
19:30b20:20 | |||||
20:30c21:20 |
授課教師:國外學者Prof. Hsin-Hsiung Bill Huang,上課時間詳課綱
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