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Python 机器学习实践:测试驱动的开发方法
book

Python 机器学习实践:测试驱动的开发方法

by Matthew Kirk
January 2018
Intermediate to advanced content levelIntermediate to advanced
211 pages
8h 31m
Chinese
China Machine Press
Content preview from Python 机器学习实践:测试驱动的开发方法
90
6
这看起来似乎没什么用,但实际上可以忽略
s
1
,…,
s
k
在第一个参数概率中的部分。
因为这些概率都属于有向分割
(D-Separated)
的。在此不会过多讨论有向分割
译注
1
因为假定在我们的马尔可夫模型中可以忽略这些变量,因为这些变量与我们的概率模
型无关:
p e
k
s p e
k
, s
which we can actually split into two separate pieces using the probability chain rule:
p s
k +1
, s
k +2
, , s
n
e
k
, s
1
, s
2
, , s
k
p e
k
, s
1
, s
2
, , s
k
This looks fruitless, but we can actually forget about x
1
, , x
k
in the first probability
because the probabilities are D-Separated. I won’t discuss D-Separation too much, but
because we’re asserting the Markov assumption in our model we can effectively forget
about these variables, because they precede what we care about in our probability
model:
p e
k
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Publisher Resources

ISBN: 9787111581666