The general route is to wrap the external model in theano, and implement a grad method for it as well.
I am using PyMC3 for parameter estimation using a particular likelihood function which has to be defined. Here L is the analytic form of my Likelihood function. I have some observational data for the radial velocity(vr) and postion ® for some objects, which is imported from excel file. My priors are M and beta which is assumed to be a uniform distribution.I want to incorporate an integral integ(gamma, beta)into my likelihood function that actually is a function of one of my parameter. I tried us…
And here is two more examples from @aseyboldt :
exoplanet.ipynb
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"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [show original
pde.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"WIP\n",
"\n",
"This is an experiment about how to use fenics and PyMC3 to\n",
"sample from the posterior of a bayesian model involving\n",show original
@aseyboldt is currently writing a blog post on the later one, but I think you should have enough information to give it a go
