Conditional variance of sum of two correlated random variables Let $\theta\sim\mathcal{N}(\bar{\theta},1/\tau_\theta)$ be a normally distributed random variable, $\varepsilon\sim\mathcal{N}(0,1/\tau_\varepsilon)$ be a normally distributed noise term independent from $\theta$, and let $P$ be an affine function of $\theta$ and $\varepsilon$, i.e., $P(\theta,\varepsilon)=a+b\theta+c\varepsilon$ with $b,c\neq 0$.
I want to determine the conditional variance $Var(\theta+P|P=p)$. 
I know how to determine the conditional variance $Var(\theta|P=p)$, which should be $Var(\theta|P)=b^2/\tau_\theta+c^2/\tau_\varepsilon$, since $\theta$ and $\varepsilon$ are uncorrelated. I am not entirely sure about $Var(\theta+P|P=p)$ though, since $P$ and $\theta$ are not uncorrelated. 
Here is a guess: 
\begin{equation}
Var(\theta+P|P=p)=Var(\theta|P=p)+Var(P|P=p)+2Cov(\theta,P|P=p)\\
=Var(\theta|P=p)+2Cov(\theta,P|P=p)
\end{equation}
Now I am not sure; since conditional on $P=p$, $P$ does not vary, the covariance term
$$2Cov(\theta,P|P=p)=2E[(\theta-E[\theta|P=p])(P-E[P|P=p])|P=p]$$ 
should be zero, which implies $Var(\theta+P|P=p)=Var(\theta|P=p)$. 
Another approach that seems plausible would be
\begin{equation}
Var(\theta+P|P=p)=Var(\theta+a+b\theta+c\varepsilon|P=p)\\
=Var(\theta(1+b)+c\varepsilon|P=p)\\
\neq Var(\theta|P=p)
\end{equation}
Which is it, or is it something else entirely? If it is the latter, what is $Var(\theta(1+b)+c\varepsilon|P=p)$ explicitly? Thanks!
 A: Your first derivation leading to $Var(\theta+P|P=p)=Var(\theta|P=p)$ is correct.  Your second derivation is also correct except for the final statement 
$$Var(\theta+P|P=p) = Var(\theta(1+b)+c\varepsilon|P=p) \implies Var(\theta+P|P=p) \neq Var(\theta|P=p)$$
They could be equal, although they don't appear to be.  In fact they are.  Here is how to reason it out.  You have a relationship between $p, \varepsilon$ and $\theta$:
$$P(\theta,\varepsilon)=a+b\theta+c\varepsilon$$
Since $a$ and $b$ are constants, with knowledge of $p$, you also know what the value of the sum $b\theta+c\varepsilon$ is.  Now
$$Var(\theta(1+b)+c\varepsilon|P=p) = Var(\theta+(b\theta+c\varepsilon)|b\theta+c\varepsilon) = Var(\theta | b\theta+c\varepsilon) = Var(\theta | p)$$
as desired.
A: It is not true that $Var(\theta|P)=b^2/\tau_\theta+c^2/\tau_\varepsilon$ in these settings. Take, for example, $\epsilon\sim N(0,1)$, $P\{v=-1\}=P\{v=1\}=1/2$ (independent of $\epsilon$), and $\theta=v\epsilon$. Then
$$P\{\theta\le t\}=\frac{1}{2}P\{\epsilon\le t\}+\frac{1}{2}P\{\epsilon\ge -t\}=\Phi(t)$$
so that $\theta\sim N(0,1)$ and $Cov(\epsilon, \theta)=0$. However, $\theta$ is not independent of $\epsilon$.
Assume that $a=0$, $b=c=1$. Then $Var(\theta|P=p)=0$ for any $p\ne 0$ ($P\ne 0\Rightarrow v=1$ so that $P=2\epsilon$ and $\theta=\epsilon$).

You need to specify the joint distribution of $(\theta,\epsilon)$ either explicitly or by assuming that $\theta$ and $\epsilon$ are independent (not just uncorrelated).

In any case,
$$Var(\theta+P|P)=\mathbb{E}[(\theta+P)^2|P]-(\mathbb{E}[\theta+P|P])^2$$
$$=\mathbb{E}[\theta^2|P]-(\mathbb{E}[\theta|P])^2=Var(\theta|P)$$
