Proper way to combine conditional probability distributions of the same random variable conditioned on a discrete variable ? (based on assumptions) My question is the following. Let's say I have two probability distributions:
$f(x|b), g(x|c)$
$b$ and $c$ are discrete events while $x$ is a continuous variable, i.e., when the button b is pressed there is some distribution for the amount of rain fall the next day, $x$. 
When the button $c$ is pressed there is a different distribution of rain fall the next day, $x$. Are there any strategies for estimating the distribution of rain fall if both buttons are pressed, i.e.,
$h(x|b,c)$ ?
And, what assumptions do those strategies rest on? 
 A: Short answer
The conditional distribution $P\left(x|yz\right)$ can be expressed in terms of $P\left(x|y\right)$ and $P\left(x|z\right)$ as
$$
P\left(x|yz\right)\propto\frac{P\left(x|y\right)P\left(x|z\right)}{P\left(x\right)},
$$
where $P\left(x\right)$ is the prior on $x$ and $y$ and $z$ are conditionally independent given $x$ (see below for more details).
Long answer
Suppose we know the conditional distributions $P\left(x|y\right)$ and $P\left(x|z\right)$ which we would like to combine to obtain the distribution $P\left(x|yz\right)$. Using Bayes' theorem, we find
$$
P\left(x|yz\right)=\frac{P\left(yz|x\right)P\left(x\right)}{P\left(yz\right)}.
$$
We assume conditional independence of $y$ and $z$ given $x$ to obtain
$$
\begin{align}
P\left(x|yz\right)&=\frac{P\left(y|x\right)P\left(x\right)P\left(z|x\right)P\left(x\right)}{P\left(x\right)P\left(yz\right)}\\
&=\frac{P\left(y\right)P\left(z\right)}{P\left(yz\right)}\frac{P\left(x|y\right)P\left(x|z\right)}{P\left(x\right)}\\
&\propto\frac{P\left(x|y\right)P\left(x|z\right)}{P\left(x\right)},
\end{align}
$$
where we have dropped the first term because it is only an overall normalisation.
Note: The above relation only holds if $y$ and $z$ are conditionally independent given $x$. Intuitively, this is the case if $y$ and $z$ are independent sources of information (see below for an example).
Example
Let $x=1$ if a sportsman took a performance enhancing drug, let $y=1$ if a drug test was positive, and let $z=1$ if the sportsman won a competition. The conditional independence assumption holds because the outcome of the drug test will not affect the outcome of the competition given $x$. Note that $y$ and $z$ are not unconditionally independent because the events are coupled by cheating.
Our prior suspicion of doping is $P\left(x\right)=\left(\begin{array}{cc}0.99 & 0.01\end{array}\right)$, where the first element corresponds to $x=0$ and the second corresponds to $x=1$. We assume that the test is 95% reliable such that
$$
P\left(y|x\right)=\left(\begin{array}{cc}
0.95 & 0.05\\
0.05 & 0.95
\end{array}\right),
$$
where $y$ is the row index and $x$ is the column index. Furthermore, assume that a competitor gains a 5% advantage to win a competition by taking a performance enhancing drug such that
$$
P\left(z|x\right)=\left(\begin{array}{cc}
1-p & 1-1.05\times p\\
p & 1.05\times p
\end{array}\right),
$$
where $p=0.1$ is the probability to win a competition if the sportsman has not taken a drug.
Using Bayes' theorem and the relation derived above, the conditional probabilities that the sportsman cheated are
$$\begin{align}
P\left(x=1|y\right) &=\left(\begin{array}{cc}
0.161017 & 0.000531\end{array}\right),\\
P\left(x=1|z\right) &=\left(\begin{array}{cc}
0.009995 & 0.010498\end{array}\right),\\
P\left(x=1|yz\right) &=\left(\begin{array}{cc}
0.000531 & 0.000558\\
0.160949 & 0.167718
\end{array}\right),
\end{align}
$$
where $y$ is the row index and $z$ is the column index in the last equation. As expected, the drug test provides stronger evidence for cheating than winning a competition $P\left(x=1|y=1\right)>P\left(x=1|z=1\right)$ but both pieces of evidence provide an even stronger case for the sportsman cheating $P\left(x=1|y=1\cap z=1\right)>P\left(x=1|y=1\right)$.
A: The solution is indeterminant.  Even using p(b and c)= p(b) p(c) all we have is that the conditional density h(x|b and c)  = h(x and b and c)/p(b and c)= h(x and b and c)/[p(b) p(c)]=h(x and c|b)/p(c).  But this does nothing to relate the distribution h(x and c|b) to f(x|b) and g(x|c)
A: 
"10 'experts' forecast some pdf of risk (or an event related to
  risk) - how do you combine these to make a decision?

Assuming the experts come up with their pdfs using independent pieces of information, the unique correct way to combine the evidence is using the pointwise product of the density functions, just as we do when doing Bayesian estimation.
