Tag Archives: pymc3

Laplace approximation in PyMC3, revisited

I found an even better example of the value of Laplace approximation, and its just a small tweak to the example I did a few weeks ago: http://nbviewer.ipython.org/gist/aflaxman/6d0a9ff2441348f3a130

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Porting PyMC2 models to PyMC3

It is time for me to start doing this. Here is a StackOverflow question and answer about it, if it is time for you, too: http://stackoverflow.com/questions/30798447/porting-pymc2-code-to-pymc3-hierarchical-model-for-sports-analytics/30853077#30853077

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MCMC in Python: Gaussian mixture model in PyMC3

PyMC3 is really coming along. I tried it out on a Gaussian mixture model that was the subject of some discussion on GitHub: https://github.com/pymc-devs/pymc3/issues/443#issuecomment-109813012 http://nbviewer.ipython.org/gist/aflaxman/64f22d07256f67396d3a

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Laplace approximation in Python: another cool trick with PyMC3

I admit that I’ve been skeptical of the complete rewrite of PyMC that underlies version 3. It seemed to me motivated by an interest in using unproven new step methods that require knowing the derivative of the posterior distribution. But, it is really coming together, and regardless of whether or not the Hamiltonian Monte Carlo stuff pays off, there are some cool tricks you can do when you can get derivatives without a hassle.

Exhibit 1: A Laplace approximation approach to fitting mixed effect models (as described in http://www.seanet.com/~bradbell/tmb.htm)

http://nbviewer.ipython.org/gist/aflaxman/9dab52248d159e02b2ae

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