So far, we have been discussing constructing Markov chains. In this section, we will see how to apply these concepts in the case of our graphical models. In the case of probabilistic models, we usually want to compute the posterior probability P(Y|E = e) , and to sample this posterior distribution, we will have to construct a Markov chain whose stationary distribution is P(Y|E = e). So, the states of this Markov chain should be instantiations x of variables 
and should converge to 
.
So, for a state 
in the Markov chain, we define the kernel 
as follows:
We can see that this transition probability doesn't depend on the current value of 
of 
but only on the remaining state 
. Now, it's really easy to show that the posterior distribution 
is a stationary distribution of this process.
In graphical models, Gibbs sampling can be very easily implemented in cases where we can compute the transition probability 
efficiently. We already know the following:
