Bayesian Inference

Kam is the realest MVP when it comes to Bayesian learning design. I am but a humble learner who translated his code into Python

Imports

Set working directory to be the parent directory of this notebook



In [ ]:

    
%matplotlib inline
import os

working_directory = os.path.pardir

import sys
sys.path.insert(0, working_directory)

import matplotlib.pyplot as plt
import numpy as np
import logging

Run Kam's Script



In [ ]:

    
import t1_simulator



In [ ]:

    
plt.plot(t1_simulator.T1_VALUES, t1_simulator.WEIGHTS[99, :])



In [ ]:

    
for weight in range(t1_simulator.WEIGHTS.shape[1]):
    print("iteration: %3d Mean: %6.4f Standard Deviation: %6.4f" % (
            weight, 
            (t1_simulator.T1_VALUES * t1_simulator.WEIGHTS[weight,:]).mean(), 
            (t1_simulator.T1_VALUES *t1_simulator.WEIGHTS[weight,:]).std()
        )
    )



In [ ]:

    
t1_simulator.T1_VALUES * t1_simulator.WEIGHTS[weight,:]



In [ ]: