# Inference in a probabilistic Bayes network

Given the following Bayessian Network:

I wonder when is it reasonable to estimate $p(u\mid c)$ as

$$p(u\mid c) \approx p(c\mid w=w_1,\ldots,w_t)$$

I want to estimate that because I can't calculate $p(u\mid c)$ because $u$ is not observable. I've been looking for inference and reasoning in bayes networks but I couldn't find any inference like this.

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Are you assuming a particular type of distribution for $u and c$ or is this simply a general exercise? – jerad Dec 15 '12 at 18:57
I don't know what you mean. Could you give an example of such a distribution? – synack Dec 16 '12 at 10:17
I what is the functional form of the conditional distribution $p(u|c)$? Is $u$ a discrete variable? Gaussian? What about c? – jerad Dec 16 '12 at 21:51
Oh, all the variables are discrete. $u$ are users of a search engine, $c$ are categories where the queries that $u$ search ($q$) and webs sites that they visit ($w$) are classified. – synack Dec 17 '12 at 8:30
See this document here under the subsection "Known Structure, Partial Observability" – jerad Dec 17 '12 at 16:51

By Bayes' rule $$P(u \mid c) = \frac{P(c \mid u) P(u)}{P(c)}$$ $P(c)$ is just a normalizing constant so what you're really interested in is $$P(c \mid u) P(u)$$ The first term $P(c \mid u)$ is the likelihood. This quantity depends on your model of how $c$ is generated from $u$. The second term, $P(u)$ is your prior. This is what you believe the value of $u$ before observing data.
If you can observe $c$, then notice that this expression does not depend at all on $w$ or $q$. In other words, $u \perp w,q \mid c$ ($u$ is conditionally independent of $w$ and $q$ given $c$)
+1 Thanks for your answer. I see your point. However, I could assume that there is such a dependency, right? I mean, imagine that, in fact, $w,q$ depend on $u$. Then, as I don't have a way of knowing any probability involving $u$, could I use some reasoning pattern to infer $p(u|c)$ from $c$ and $q$?? – synack Dec 15 '12 at 9:06
@Kits89, I think tskuzzy's last statement just meant that according to your graphical model, if $c$ is observed then $w and q$ are independent of $u$. – jerad Dec 15 '12 at 18:48
I know. But in my last comment I was saying to imagine that there is an arrow from $u$ to $w$ in my graphical model. – synack Dec 16 '12 at 10:18