Mastering Python Data Analysis

Summary

We have discussed how to test models and hypotheses with Bayesian analysis using the Python package, PyMC. It is a powerful package that gives out more intuitive results, where you see how the parameters are characterized. Not all posterior distributions are shaped like Gaussian, but the trace and autocorrelation should look similar for well-constrained parameters.

In the next chapter, we will dive into some of the machine learning algorithms available in Python and look at how they can identify clusters, classify data, and do linear regression. As in this chapter, we will compare the linear fit with that of Bayesian analysis and OLS. We will compare the cluster findings with the analysis that we did of galaxies in the universe in Chapter ...