Bayesian update of a confidence interval How does one update a confidence interval using Bayes rule?
Say, for example, an experiment shows that the mean lies in [A, B] with 95% confidence. Later, a colleague says they ran a similar experiment and found that the mean lies in [C, D] with 95% confidence (or any other CI).
How does one "merge" the two data under Bayes?  
Please help out with any misconceptions, I'm not a trained statistician.
 A: So first of all, this is dependent on the distribution of your data. This question can be very tricky for certain distributions. However, generally when working with confidence intervals, a normal distribution is assumed, simplifying things.
To calculate the confidence interval, you would need to know the number of observations for both experiments.
The 95% confidence interval is namely defined as:
mean +- 1.96 * standard_deviation/sqrt(observations).
If you would know this you can calculate the means simply by:
$$mean_1 = (A+B)/2$$
$$mean_2 = (C+D)/2$$
$$std_1 = (A-B)/2/1.96*\sqrt(n_1)$$
$$std_2 = (A-B)/2/1.96*\sqrt(n_2)$$
Now the new mean needs to be normalized according to observations
$$mean = (mean_1*obs_1 + mean_2*obs_2)/(obs_1 + obs_2)$$
The standard deviation can now be calculated from the number of samples, the
$$std = \sqrt{\frac{n_1std_1^2 + n_2std_2^2 + n1(mean_1-mean)^2 + n_2(mean_2-mean)^2}
{n_1+n_2}}$$
For quick derivation (for example):
Is it possible to find the combined standard deviation?
And the interval
$$A = mean - 1.96 * std / \sqrt(n1+n2) $$
$$B = mean + 1.96 * std / \sqrt(n1+n2) $$
If you don't know the amount of samples you can always still assume a certain underlaying distribution.
Most often experiments can be approximated by a normal distribution.
Having made this assumption you can calculate the variance and mean of both from the probability density function.
Add the two functions and normalize so that the integral equals one gives the new density function from which you can calculate the 95% confidence interval.
