分解
计算机科学
简单(哲学)
管理科学
决策论
光学(聚焦)
决策问题
余数
航程(航空)
贝叶斯概率
影响图
人工智能
数学
算法
决策树
工程类
认识论
生态学
光学
物理
航空航天工程
统计
生物
哲学
算术
出处
期刊:Cambridge University Press eBooks
[Cambridge University Press]
日期:2010-09-23
卷期号:: 169-198
标识
DOI:10.1017/cbo9780511779237.007
摘要
So far this book has given a systematic methodology that can be used to address and solve some simple decision problems. However some of the most interesting and challenging real decision problems can have many facets. It is therefore necessary to extend the Bayesian methodology described earlier in the book so that it is a genuinely operational tool for addressing the types of to complex decision problems regularly encountered. Even for moderately sized problems we have seen the advantages of disaggregating a problem into smaller components and then using the rules of probability and expectation within a suitable qualitative framework to draw the different features of a problem into a coherent whole. Although the appropriate decomposition to use depends on the problem addressed there are nevertheless some well-studied decomposition methods that are appropriate for a wide range of decision problem which the analyst is likely to encounter frequently. The remainder of this book will focus on the justification, description and enaction of some of these different methodologies.
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