I am using pyro version 0.1.2 and run the bayesian regression example from the tutorial section. Either there is something odd, or I don’t understand what is supposed to happen:

Looking at the uncertainty of the estimated parameters if I run the code as provided on the website I get similar results:

Now I wanted to see if I can influence the estimated uncertainty by changing the number of data-points, changing noise in the data or changing the uncertainty in the prior. It seems that only changing the uncertainty in the prior has an effect, but neither changing noise in the data, nor using fewer data-points (e.g. 2 or 3 as compared to some 10k).

Is this the expected behavior? I thought having fewer data or data with higher noise should also increase the uncertainty over estimated parameters!

Additionally, there seems to be a small error in the example code:

The function

def build_linear_dataset(N, noise_std=0.1):

takes noise_std as second parameter, but further down where the function is called it is assumed that the second parameter would be num_features