python - How to correctly defined mixture of Beta distributions in PyMC3 -


i trying fit data using mixture of 2 beta distributions (i not know weights of each distribution) using mixture pymc3. here code:

model=pm.model() model:     alpha1=pm.uniform("alpha1",lower=0,upper=20)     beta1=pm.uniform("beta1",lower=0,upper=20)     alpha2=pm.uniform("alpha2",lower=0,upper=20)     beta2=pm.uniform("beta2",lower=0,upper=20)     w=pm.uniform("w",lower=0,upper=1)     b1=pm.beta("b1",alpha=alpha1,beta=beta1)     b2=pm.beta("b2",alpha=alpha2,beta=beta2)     mix=pm.mixture("mix",w=[1.0,w],comp_dists=[b1,b2])

after run code following error: attributeerror: 'list' object has no attribute 'mean'. suggestions?

pymc3 comes pymc3.tests module contains useful examples. searching directory word "mixture" came upon this example:

mixture('x_obs', w,         [normal.dist(mu[0], tau=tau[0]), normal.dist(mu[1], tau=tau[1])],         observed=self.norm_x)

notice classmethod dist called. googling "pymc3 dist classmethod" leads doc page explains

... each distribution has dist class method returns stripped-down distribution object can used outside of pymc model.

beyond i'm not entirely clear why stripped-down distribution required here, seems work:

import pymc3 pm  model = pm.model() model:     alpha1 = pm.uniform("alpha1", lower=0, upper=20)     beta1 = pm.uniform("beta1", lower=0, upper=20)     alpha2 = pm.uniform("alpha2", lower=0, upper=20)     beta2 = pm.uniform("beta2", lower=0, upper=20)     w = pm.uniform("w", lower=0, upper=1)     b1 = pm.beta.dist(alpha=alpha1, beta=beta1)     b2 = pm.beta.dist(alpha=alpha2, beta=beta2)     mix = pm.mixture("mix", w=[1.0, w], comp_dists=[b1, b2])

note when using dist classmethod, name string omitted.


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