Hi! I was wondering if you managed to get the Salary program (two way anova) working. I'm trying really hard but I don't seem to get an answer similar to the book. (for example there's no main effect on FT1 FT2 and FT3, plus so many convergence problems)

Why is NUTS used as the step method for kappa? Using Metropolis appears to produce the same results, but runs faster.

I am new to pymc3 and trying to understand when to use NUTS.

There are small typos in:

• 06_BernGrid.py
• 08_BernTwoGrid.py
• 15_YmetricXsinglePyMC.py
• 16_SimpleRobustLinearRegressionPyMC.py

Fixes:

• Should read approximation rather than aproximation.
• Should read precision rather than precission.
• Should read biased rather than baised.

Semi-automated pull request generated by https://github.com/timgates42/meticulous/blob/master/docs/NOTE.md

https://github.com/aloctavodia/Doing_bayesian_data_analysis/blob/master/16_SimpleLinearRegressionPyMC.py

Hi! I've tried to do linear regression by myself, using the following model (very much similar to the book), but no matter what I do, I can't manage to get a straight fit.

I've seen that in your code you do a transformation. Why is that? Is there any way to get the result without resorting to the transformation?

import numpy as np
import pymc as pm

true_intercept = 10
true_slope = 0.5 

x = np.linspace(0, 10, 100)
y = true_slope * x + true_intercept
# Jitter x and y
x += 0.5 * (np.random.rand(len(x)) * 2.0 - 1.0)
y += 1.0 * (np.random.rand(len(x)) * 2.0 - 1.0)
scatter(x, y)
show()
# Linear regression
b0 = pm.Normal('b0', 0.0, tau=1e-5, value=0)
b1 = pm.Normal('b1', 0.0, tau=1e-5, value=0)

@pm.deterministic
def mu(value=x, b1=b1, b0=b0):
    return x * b1 + b0

tau = pm.Gamma('tau', 0.01, 0.01)

resp = pm.Normal('y', mu, tau, value=data, observed=True)

mcmc = pm.MCMC([b0, b1, mu, tau, resp])
mcmc.sample(40000, burn=20000, thin=10)

• fixed IPython/Kruschkes_Doing_Bayesian_Data_Analysis_in_PyMC3.ipynb
  • StringIO and BytesIO in 18_ section requires import from six to work in both, Python 2 and 3
• fixed 18_*.py
  • subplot count starts at 1, not 0
• added _process_all.py, changed some *.py
  • script for processing with ipyparallel (requires 'ipycluster start' action before)
  • adapted imports because of namespace peculiarities of multiprocessing function handling

Hi Osvaldo, as we discussed, here is a pull request for the ipython notebook I created. This is my first foray into committing into someone else's repo, so I have no idea if I am doing the pull request right... Best, Brian

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