Why not use pm.Interpolated
? You’ll need one of these for each sample – something like
def gen_interp(name, vals, h=0.2, hw=1.5, k=25, plot=True, shape=1):
xmx, xmn = np.max(vals), np.min(vals)
xrange = xmx - xmn
x_interp = np.linspace(xmn-h*xrange, xmx+h*xrange, k)
y_interp = np.concatenate([[0], sp.stats.gaussian_kde(vals)(x_interp), [0]])
w = hw*xrange
x_interp = np.concatenate([[xmn-w], x_interp, [xmx+w]])
if plot:
plt.plot(x_interp, y_interp)
return pm.Interpolated(name, x_interp, y_interp, shape=shape)
with pm.Model() as mod:
randeff = pm.Normal('random', 0., 1.)
migration_rnd = tt.zeros(n_sample, dtype=theano.config.floatX)
for j in range(n_sample):
migration_rnd = tt.set_subtensor(migration_rnd[j],
gen_interp('sample_%d' % j, sample_densities[j]))
intercept = pm.Normal('intercept', 0., 1.)
err_sd = pm.HalfNormal('err_sd', 1.)
y_pred = pm.Deterministic('y', migration_rnd * randeff + intercept)
y_obs = pm.Normal('lik', mu=y_pred, sd=err_sd, observed=Y)
trace = pm.sample(500, tune=750, chains=9, cores=3, nuts_kwargs={'target_accept': 0.9, 'max_treedepth': 20})
or full gist:
empirical_marginals.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
This file has been truncated. show original
And if the samples all share the same marginal density (i.e. it’s a population estimate) you can use
migration_rnd = gen_interp('sample_effs', x_migration_density, shape=(n_sample,))
as a shortcut