Say, there are 3 categories being selected with probability $\theta_i$ , $i=1,2,3$. After $n$ independent multinomial trails, we observe say $n_i$ outcomes of each $i$ category.
Then, someone told me that actually we can know $\theta_2=0.1$ for certain.
Now, I am only interested in the $\theta_1$, i.e. I would like to know $E(\theta_1\mid n_1,n_2,n_3)$. To do the Bayesian inference, I see two ways:
Via the conditional distribution: Conditional on $\theta_2=0.1$, I can constuct a new binomial model with probability $p=\frac{\theta_1}{1-0.1},1-p=\frac{\theta_3}{1-0.1}$, and calculate the $E(\theta_1\mid n_1, n_3)$, ignoring the evidence of $n_2$. In other words, I am observing $n_1$ out of $n_1+n_3$ trails is category $1.$
Via the marginal distribution: the marginal distribution of $\theta_1$ is again a binomial one with probability $\theta_1$ and $(1-\theta_1)$. Then I can calculate $E(\theta_1\mid n_1,n_2,n_3)$. In other words, I am observing $n_1$ out of $n_1+n_2+n_3$ trails is category $1.$
Note, in both the two ways above, I can assume the same margianl prior distribution for $\theta_1$ as $f_{\theta_1}()$ (so easily, I know the $f_p()$ needed in $1.$).
At the beginning, I thought both of them should give me the same result as I cannot see anything wrong in them, but I find out they are different (by playing with a two point prior distribution for $f_{\theta_1}$).
Now I see, it seems I am ignoring some very useful information in the 2nd way, i.e. the fact I know $\theta_2=0.1$ for sure. Thus they are different.
My questions are:
- Are my observations/conclusions so far all correct?
- Is there any way to incoperate $\theta_2=0.1$ in the 2nd approach above?
- Generally, the 1st way of reasoning is useless, right? i.e. the property that a conditional distribution of a multinomial distribution is still a multinomial is useless. Since in realisty we barely know something like $\theta_2=0.1$ for sure. Or is there any good example of application here?
Hope I am making sense...Thanks.