Chris - 1 year ago 135

Python Question

I'm trying to use PyMC3 to solve a fairly simple multinomial distribution. It works perfectly if I have the 'noise' value set to 0.0. However when I change it to anything else, for example 0.01, I get an error in the find_MAP() function and it hangs if I don't use find_MAP(). Is there some reason that the multinomial has to be sparse?

import numpy as np

from pymc3 import *

import pymc3 as mc

import pandas as pd

print 'pymc3 version: ' + mc.__version__

sample_size = 10

number_of_experiments = 1

true_probs = [0.2, 0.1, 0.3, 0.4]

k = len(true_probs)

noise = 0.0

y = np.random.multinomial(n=number_of_experiments, pvals=true_probs, size=sample_size)+noise

y_denominator = np.sum(y,axis=1)

y = y/y_denominator[:,None]

with Model() as multinom_test:

probs = Dirichlet('probs', a = np.ones(k), shape = k)

for i in range(sample_size):

data = Multinomial('data_%d' % (i),

n = y[i].sum(),

p = probs,

observed = y[i])

with multinom_test:

start = find_MAP()

trace = sample(5000, Slice())

trace[probs].mean(0)

Error:

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Answer Source

This works for me

```
sample_size = 10
number_of_experiments = 100
true_probs = [0.2, 0.1, 0.3, 0.4]
k = len(true_probs)
noise = 0.01
y = np.random.multinomial(n=number_of_experiments, pvals=true_probs, size=sample_size)+noise
with pm.Model() as multinom_test:
a = pm.Dirichlet('a', a=np.ones(k))
for i in range(sample_size):
data_pred = pm.Multinomial('data_pred_%s'% i, n=number_of_experiments, p=a, observed=y[i])
trace = pm.sample(50000, pm.Metropolis())
#trace = pm.sample(1000) # also works with NUTS
pm.traceplot(trace[500:]);
```

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