I asked a similar question here, but this time I’d like know if there’s a way to add the logp values of accepted points to the trace as we sample, so that we don’t have to evaluate it twice?

You are asking some great questions These should go into our docs I think…

There is two way to do it, the easiest is to create a Deterministic RV to save the logp. This is what we do in SMC currently:

A more complicated way is to put the logp into the sampler statistics of the trace, there is a discussion here:

https://github.com/pymc-devs/pymc3/pull/2339#pullrequestreview-45401866

Interesting thread! Is there any way to save the logp for each data point as an array? So that each element becomes a vector instead of a scalar?

Depending on how you define the logp for each data point (i.e., element-wise logp), you can computed it using the logprob function of the observed conditioned on the posterior using:

Thanks for you help. Additionally, is it possible to use this capability with a new set of observed values (y)?

I want to make a dataframe with the following columns: [parameter 1, parameter 2, Y, (log)likelihood]

This will allow me to plot multiple paths of the PDF of the distribution in a Baysian way. I could not find this functionality in the package. Thanks!

I dont think there is built in function for that. I would probably extract the parameters from the trace by hand and compute the logp conditioned on the new observation by hand.

I know this is an old issue, but I thought I’d share a solution to compute the logp of a set of new observations. You can use the same trick as above and define a Deterministic RV for the logp. Then use sample_posterior_predictive. Assuming you’ve already produced a posterior sample `trace`

, and `y_future`

is your new observed variable, use the following code:

```
with model:
llk = pm.Deterministic('llk', y_future.logpt)
logp = pm.sample_posterior_predictive(trace, vars=[llk], keep_size=True)
```

Set `keep_size=True`

to compute the logp for each sample of the trace and retain the shape ((n_chains, n_samples)).