title: How to update observations over time in PyMC3?
body: <p>I'm in the process of teaching myself PyMC3 and I was replicating <a href="http://people.duke.edu/~ccc14/cspy/16C_PyMC3.html#Changepoint-detection" rel="nofollow noreferrer">this changepoint detection example</a>. I want to extend the example by sampling the posterior of the 2 different Poisson rate parameters over time. How would I go along doing this?</p>
  
  <p>To give a bit more detail: if I have the time-series <code>y</code> from the example. I want to first sample with observations <code>y[0:2]</code>, then <code>y[0:3]</code>, <code>y[0:4]</code>, ... , <code>y[0:100]</code>. My goal is to plot the distribution of <code>mu0</code> and <code>mu1</code> (the Poisson rate parameters) over time 2..100.
  Obviously, I should be able to do this by creating around 98 different models and sampling independently from each of one. However, this seems rather inefficient and I'm wondering if there is a better way of just updating the model with new observations and continuing the trace?</p>
  
  <p>I couldn't find anything related in the documentation or online, so I tried just replacing the variables but then I get a "ValueError: Variable name changepoint already exists.".</p>
  
  <p>TLDR; Is there an efficient way to update observations over time and keep on sampling from the previous trace with these new observations?</p>