Combining information from independent experiments: further ideas

2017 April 19
by Daniel Lakeland

It's an interesting question connected to something posted at Andrew's blog. I posted one possible "solution" using Stan previously, and Corey and I did some exploration of the issues in the comments.

Here are some new thoughts I've had, but first the set up. There are N labs each of which investigate a phenomenon in which there is a fundamental ratio R = X/Y and X and Y are individually measured with error. The labs each publish some posterior distribution for X and Y. In the example problem we have

Xtrue[i] ~ normal(Xbar[i],sx[i]); // published by lab i

Ytrue[i] ~ normal(Ybar[i],sy[i]); //similar

for each lab i

Now we'd like to do inference from this on the ultimate quantity of interest R. We assume that there is an underlying Rtrue of interest, and that in each lab, due to peculiarities of their apparatus etc an underlying R[i] is at work so that Xtrue[i]/Ytrue[i] = R[i] and R[i] is "close to" Rtrue.

R[i] ~ normal(Rtrue,deltaR);

with deltaR "small" and Rtrue a parameter in our model.

How can we do inference on Rtrue?

The thing that I recently came to is that in this situation, we don't get the individual data points from the labs, we get as *data* only Xbar[i] and Ybar[i] the published values that describe the lab's posterior distributions over Xtrue[i] and Ytrue[i], so we can do something like as follows:

Rtrue ~ normal(OurRtrueGuess, someSpread);// our prior for Rtrue

R[i] ~ normal(Rtrue, deltaR);// our prior for R[i] given Rtrue, expresses the "closeness" of the individual experiments to the real R value

Ytrue[i] ~ normal(Ybar[i], sy[i]); // the published posterior probability distribution over Ytrue[i] from lab i

Xbar[i] ~ normal(R[i]*Ytrue[i],sx[i]); 
/* the likelihood of having Xbar be published
 as the estimate of Xtrue by lab i given 
Ytrue[i], R[i] and the lab published sx[i] inference error. 
That is: p(Xbar[i] | R[i],Ytrue[i])

So now what we've got is essentially p(R) p(R[i]|R) p(Ytrue[i] | Data[i]) p(Xbar[i] | R[i], Ytrue[i], Data[i])

the narrow width of p(R[i]|R) is what describes the fact that all the labs are trying to set up their apparatus to reproduce carefully a single ratio R. The Ytrue[i] distribution is as given from the lab, and finally the likelihood over the Xbar value is potentially either the published distribution from the lab, or if we have additional information, it could be some modified thing. When Xbar is a sufficient statistic and we're using a normal distribution, it can be as good as getting the whole dataset from the lab.

What's interesting is, in these cases, the output of each labs inference process becomes *observed data* that we use in a likelihood type factor in our Bayesian model for combining the data.


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