# Gibbs sampling how to sample from the conditional probability? Bayesian model

I want to learn Gibbs sampling for a Bayesian model. How can I sample the variable from the conditional distribution? In this example, arrow means dependent; for example, Grade depends on Difficulty and Intelligence. To use Gibbs sampling to calculate the joint distribution, first I set the Difficulty and Intelligence to (1,1).
The next step is to sample Grade from the $\rm{P(Grade|Difficulty=1,Intelligence=1)}$, but how can I sample?

• This depends on what the conditional distributions are, which in turn is determined by your model. More details needed. Apr 2, 2014 at 14:49
• Could you edit the clarifying information into your question? Apr 2, 2014 at 21:23

Since we are calculating the joint distribution, we'll assume that our initial sample is $x = P(D=0,I=0,G=0,L=0,S=0)$ .

To calculate the next sample, we'll need to sample each variable from the conditional distribution.

1. $P(D\mid G,I,S,L)$,from the conditional independencies in the Bayes net, simplifies to just sampling $P(D)$. We sample and get the value $D=1$.
2. Similarly for $I$, we sample and get the value $I=1$.
3. Sampling for $P(G\mid D,I,S,L)$, due to the conditional independencies encoded by the Bayes net, simplifies to $P(G\mid D,I)$. Since we have already sampled $D=1,I=1$, we use those values and sample $P(G\mid D=1,I=1)$. In the CPD for Grade, we can choose one of the value from the last row (where $D=1,I=1$). We sample and get the value $G=2$ (the value 0.3)
4. $P(L\mid I, G,D,S)$ simplifies to $P(L\mid G)$. We sample from the second row the Letter CPD, where $G=2$, and we sample and get $L=1$ (the value 0.6).
5. Similarly, sample $P(S \mid I,L, G,D,)$ by simplifying to $P(S \mid I)$. We get $S=1$ (sampling from the second row of the CPD where $I=1$.

And we'll have a new sample $x': P(D=1,I=1,G=2,L=1,S=1)$.

• Thank you very much for your answer! At the beginning I am not clear about how can I sample? I think it is not right to chose the one that with bigger P. The key point is to chose a properway to define its distribution according the given CPD. Apr 29, 2014 at 19:55
• I didn't get what you meant by "it is not right to choose the one with the bigger P". In a CPD (such as $P(L \mid G)$ ), any one of the assignments is chosen at random, not the one with the highest probability. Apr 30, 2014 at 10:03
• Yes, you are right, the assignments is chosen randomly based on the CPD :)) May 1, 2014 at 19:21
• @shark8me When you say sample $P(D)$, to clarify, you mean sample a number from [0, 1] with uniform distribution, and use that number (probability) to map to a value of D using the associated CDF, right? May 13, 2016 at 20:48

If the conditional distribution cannot be generated directly from standard random generators, you can apply a Metroplolis-Hastings schema within the Gibbs sampler.

1. draw one sample, $D_t$, from $P(D|G_{t-1},I_{t-1},S_{t-1},L_{t-1})$ using Metropolis sampler with initial value $D_{t-1}$
2. draw one sample, $G_t$, from $P(G|D_{t},I_{t-1},S_{t-1},L_{t-1})$ using Metropolis sampler with initial value $G_{t-1}$
3. draw one sample, $I_t$, from $P(I|D_{t},G_{t},S_{t-1},L_{t-1})$ using Metropolis sampler with initial value $I_{t-1}$

...

reference: Introduction Monte Carlo Methods with R chapter 7