Baum-Welch算法(EM算法)对HMM模型的训练

Baum-Welch算法就是EM算法，所以首先给出EM算法的Q函数

\[\sum_zP(Z|Y,\theta')\log P(Y,Z|\theta) \]

换成HMM里面的记号便于理解

\[Q(\lambda,\lambda') = \sum_zP(I|O,\lambda')\log P(I,O|\lambda) \]

根据状态序列和观测序列的联合分布

\[\begin{align*} P(O,I|\lambda) &= \sum_IP(O|I,\lambda)P(I|\lambda)\\ &= \pi_{i_1}b_{i_1}(o_1)a_{i_1i_2}b_{i_2}(o_2)\dots a_{i_{T-1}i_T}b_{i_T}(o_T)\\ \end{align*}\]

代入上式后得

\[\begin{align*} Q(\lambda, \lambda') &= \sum_IP(I|O,\lambda')\log\pi_{i_1}\\ &+ \sum_IP(I|O,\lambda')\log\sum_{t=1}^Tb_{i_t}(o_t) \\ &+ \sum_IP(I|O,\lambda')\log\sum_{t=2}^Ta_{i_{t-1}i_T} \end{align*}\]

这便是E步，下面看看M步.

看Q函数得第一步， 由于带有约束

\[\sum_i^N\pi_i = 1 \]

这个时候就需要请出拉格朗日乘子了

\[\begin{align*} L &= \sum_IP(I|O,\lambda')\log\pi_1 + \gamma(\sum_{i=1}^N\pi_i -1)\\ &= \sum_{i=1}^NP(O,i_1=i|\lambda')\log\pi_i + \gamma(\sum_{i=1}^N\pi_i -1)\\ \end{align*}\]

令\(\dfrac{\partial L}{\partial\pi_i} = 0\)得到

\[\begin{align*} P(O, i_1 = i|\lambda') + \gamma \pi_i &= 0\\ P(O, i_1 = i|\lambda') &= -\gamma \pi_i\\ \sum_{i=1}^NP(O, i_1 = i|\lambda') &= -\gamma \sum_{i=1}^N\pi_i\\ \gamma &= -P(O|\lambda') \end{align*}\]

回代，得到

\[\pi_i = \dfrac{P(O, i_1=i|\lambda')}{P(O|\lambda')} \]

其他得参数同样可以得到