更改

跳到导航 跳到搜索
添加122字节 、 2024年9月18日 (星期三)
无编辑摘要
第161行: 第161行:  
</math>
 
</math>
   −
The state transition matrix can be viewed as the dynamics of the Markov chain because the probability distribution of the state at any time t+1, Pr(Xt​), can be uniquely determined by the probability distribution of the state at the previous time t and the state transition matrix, satisfying the relationship: Pr(Xt+1​=j)=∑i=1N​pij​⋅Pr(Xt​=i), where i,j∈X are arbitrary states in X, and N=#(X), the total number of states in X.
+
The [[Transitional Probability Matrix]] can be viewed as the [[Dynamics]] of the [[Markov Chain]] because the probability distribution of the state at any time [math]t+1[/math], [math]Pr(X_t)[/math], can be uniquely determined by the state probability distribution at the previous moment, i.e., [math]Pr(X_t)[/math] and [[Transitional Probability Matrix]], satisfying the relation:
    
<math>
 
<math>
 
Pr(X_{t+1}=j)=\sum_{i=1}^N p_{ij}\cdot Pr(X_t=i),
 
Pr(X_{t+1}=j)=\sum_{i=1}^N p_{ij}\cdot Pr(X_t=i),
 
</math>
 
</math>
 +
 +
Here [math]i,j\in \mathcal{X}[/math] are all arbitrary states in [math]\mathcal{X}[/math], and [math]N=\#(\mathcal{X})[/math] is the total number of states in [math]\mathcal{X}[/math].
    
The following table presents three different transition probability matrices and their EI values:
 
The following table presents three different transition probability matrices and their EI values:
2,359

个编辑

导航菜单