A Revealing Introduction to Hidden Markov Models
A Revealing Introduction to Hidden Markov Models
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DOI：
10.1201/9781315213262-2
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发表时间：
2017-09
期刊：
影响因子：
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通讯作者：
M. Stamp
中科院分区：
文献类型：
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作者：
M. Stamp
假设我们想要确定地球上某一特定地点连续几年的年平均温度。为了使它更有趣，假设我们所关心的年代是在遥远的过去，在温度计被发明之前。既然我们不能回到过去，我们就寻找温度的间接证据。为了简化问题，我们只考虑两种年温度，“热”和“冷”。假设现代证据表明，一个炎热的年份之后又一个炎热的年份的概率是0.7，而一个寒冷的年份之后又一个寒冷的年份的概率是0.6。我们假设这些概率在遥远的过去也成立。目前的信息可以总结为hc
Suppose we want to determine the average annual temperature at a particular location on earth over a series of years. To make it interesting, suppose the years we are concerned with lie in the distant past, before thermometers were invented. Since we can’t go back in time, we instead look for indirect evidence of the temperature. To simplify the problem, we only consider two annual temperatures, “hot” and “cold”. Suppose that modern evidence indicates that the probability of a hot year followed by another hot year is 0.7 and the probability that a cold year is followed by another cold year is 0.6. We’ll assume that these probabilities held in the distant past as well. The information so far can be summarized as H C