Solved – Setting PyMC model with two different time series data

I've been working with PyMC for a bit, and am stuck on this one. I see example on fitting time series, in the tutorial and others like:


However, in each case, we either have a single variable, or data for two or more variables for the same time points (so the vectors are all of the same length). What happens if my data is like:

t=[1,2,3] x=[.5,.8,.2] 


t=[1.5,2.5] y=[.1,.5] 

with some simple model, like

x=a*t+b y=c*t+d 

the model isn't important. the point is that the "observed" data have different time points. I haven't been able to find any example like this, without perfectly aligned data. Are there any? Is there a simple example that someone could point out?


 Brian Blais 


I may be misunderstanding what you are after, but if you are modeling time t explicitly, it should not matter that they are not aligned, does it? You might have a peek in Chapter 11 of Gelman and Hill for examples of Bayesian models for longitudinal data.

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