Bayesian Theory
Bayesian Theory
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DOI：
10.1007/3-540-28820-1_2
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发表时间：
2001
影响因子：
2.4
通讯作者：
José M Bernardo and Adrian F M Smith-José-M-Bernardo-and-Adrian-F-M-Smith-2177748935
中科院分区：
文献类型：
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作者：
José M Bernardo and Adrian F M Smith-José-M-Bernardo-and-Adrian-F-M-Smith-2177748935
在本章中，我们回顾了与贝叶斯推理相关的概率演算的关键恒等式。然后，我们研究三个基本的上下文参数建模，即（i）离线推理，（ii）在线推理的时不变参数，和（iii）在线推理的时变参数。在每种情况下，我们使用贝叶斯框架来推导正式的解决方案。每个上下文将在后面的章节中详细讨论。
In this Chapter, we review the key identities of probability calculus relevant to Bayesian inference. We then examine three fundamental contexts in parametric modelling, namely (i) off-line inference,(ii) on-line inference of time-invariant parameters, and (iii) on-line inference of time-variant parameters. In each case, we use the Bayesian framework to derive the formal solution. Each context will be examined in detail in later Chapters.