In [1]:

    

import numpy as np
from tensorprob import Model, Parameter, Normal


    

In [2]:

    

with Model() as model:
    mu = Parameter()
    sigma = Parameter(lower=0)

    X = Normal(mu, sigma)

model.observed(X)
model.initialize({
    mu: 0,
    sigma: 1,
})


    

In [3]:

    

%matplotlib inline
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D


    

In [4]:

    

xs = np.linspace(-5, 5, 200)
plt.plot(xs, model.pdf(xs), 'b-')


    

    
Out[4]:





[<matplotlib.lines.Line2D at 0x7f80941983c8>]






    

In [5]:

    

data = np.random.normal(0, 1, 1000)
@np.vectorize
def nll(mu_, sigma_):
    model.assign({mu: mu_, sigma: sigma_})
    return model.nll(data)

ax = plt.gcf().add_subplot(111, projection='3d')
mu_, sigma_ = np.meshgrid(np.linspace(-.5, .5, 20), np.linspace(.5, 1.5, 20))
ax.plot_wireframe(mu_, sigma_, nll(mu_, sigma_))


    

    
Out[5]:





<mpl_toolkits.mplot3d.art3d.Line3DCollection at 0x7f806193ce80>






    

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