The current `GaussianRandomWalk`

distribution seems to have the implicit assumption that data are regularly spaced in time. Would it be possible to implement a version (maybe called `GaussianRandomIrregular`

?) where you can specify a vector of time points for which you have observations? The pdf would have to take that into account with the `sd`

, but I think that would just be `sqrt(sd*dt)`

because of the variance sum law, where `dt`

is the time between current and previous observations?

I had a quick look at the `GaussianRandomWalk`

class, but I’m still on my training wheels with both python and PyMC3, so don’t feel confident on how to implement this. Would be interested in whether this is a vaguely sensible idea?

# Variant of GaussianRandomWalk distribution?

**drbenvincent**#1

**drbenvincent**#2

Or maybe this is a silly idea. Maybe the approach is to have a latent GRW for regularly spaced time points, but which you just have observations for irregular time points?

**gBokiau**#3

I think the real trouble here is data. You would need a function that perhaps takes one argument for t’s or dt’s and one for x’s?

If the data is simply sparse but regular (or somewhat so), a masked array (NA’s where data is missing) would work out of the box.

I’m insufficiently well-read on GRW to be sure, but if you stand by `sqrt(sd*dt)`

, your problem is solved, eg simply use

`pm.GaussianRandomWalk(sd=tt.pow(dt, sd, 0.5), mu=0, observed=x)`