I’m trying to create a custom likelihood function similar to the one described here : P(d|m) \propto exp \frac{-||d-f(m)||^2}{\sigma^2} where again d is observed data and m are parameters. In my case however, f involves a numerical integration, something like f(\lambda, m) \propto \int{\eta(\lambda)…

Thanks for replying! I maybe could have done a better job of calling out the links, but I already referenced the post you mentioned, which I’m fairly well acquainted with at this point. I think my situation might be different enough to make previous approaches ineffective. I think I’m looking for …

Oh right. I think you might be able to change the quad integrator in the post with scipy.integrate.trapz - maybe you can give this a try?

Thanks @junpenglao . This is probably not as simple as just replacing “quad” with “trapz,” right? I assume I will also have to edit the whole Integrate class, including the grad() function. The role of Theano Ops and their grad() functions remain a bit mysterious to me. I can spend more time with the…

[image] ceverson3: This is probably not as simple as just replacing “quad” with “trapz,” right? It should work as the gradient of a integrate class is just the original function itself. So as long as it is some integrate approximation, you should be able to just replace the approximation.

Maybe too many years late. But I run into the same problem and followed @junpenglao 's advice and tried adapting numpy.trapz. I simply replaced np by at (ie. numpy by pytensor) and I commented out the exception rule (just for the sake of this example but may break things up in more complex uses). Re…

Implementing something like tt.trapz() for numerical integration over a set domain?

Questions

junpenglao August 27, 2019, 5:33am 2

We have some post using numerical integration: Custom theano Op to do numerical integration you can take a look at try the theanoOps from that post.

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Implementing something like tt.trapz() for numerical integration over a set domain?

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