identifying latent variables in this model

I have been trying to understand EM and I am having a hard time understanding what a latent variable is. In particular, I am having issues in identifying whether in a particular model that I am using, a particular variable is a latent variable or not.

So, basically I observe two images $x$ and $y$ and I am trying to estimate a transformation $t$ between them which is parameterised by a set of parameters $w$. So the model is: $$y = t(x, w) + e$$ So, $y$ is a transformed version of $x$ according to some transformation parameters $w$. Graphically, I can represent it as follows.

Please ignore the prior distributions on $w$ and $\phi$ and all the hyper parameters.

So, my understanding is that $x$ and $y$ are two random variables but they are not latent as we actually observe and measure particular instances of them (hence they are shaded in the PGM diagram).

However, the parameters of the transformation $w$ can be treated as a random variable and since we do not observe it but rather want to infer it, they are the latent variables in my model.

Is my understanding/reasoning correct?

Hm... if you have data on $x$ and $y$ and assume some distribution of the error given $x$ then I would just maximize log likelikhood. I don't think there is any latent variable then.
But maybe you are not given (or don't want to assume) anything about the parameters of $P(e|x)$. If that is the case then it seems to me that parameters of that distributions would be latent variables.
If you do not want to assume anything about $P(e|x)$ then I don't think you can estimate w's. This is because whatever w's you choose you will always find a distribution $P(e|x)$ that will make the model fit perfectly.
• Thanks for the answer! I am assuming that the noise model is a zero mean IID, so the noise variance is being inferred as well (denoted by the variable $\sigma$). My confusion is why is $w$ not a latent variable as you say when I assume zero mean IID noise. $w$ is still being inferred and is not measured/observed. – Luca Aug 11 '14 at 22:15
• There is a distribution attached to $w$ and it may have some complex form a-posteriori. So in a sense it is not a constant and can take a range of values with a certain probability. – Luca Aug 11 '14 at 23:16