#!/usr/bin/env python
# coding: utf-8

# In[1]:


import numpy as np
import pymc3 as pm


# Simulate data from Gaussian with mean=2 and SD=5 and try to recover these parameters with PyMC3.

# In[2]:


x = np.random.normal(2, 5, size=1000)


# In[3]:


with pm.Model() as model:
    
    mu = pm.Normal('mu', 0, sd=1e4)
    sigma = pm.HalfCauchy('sigma', 25)
    
    likelihood = pm.Normal('likelihood', mu, sd=sigma, observed=x)


# In[5]:


with model:
    tr = pm.sample(1000)


# In[6]:


pm.summary(tr)