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  3. HarvardX Data Science Module 4 - Inference and Modeling

Exercise

Exercise 8 - Estimate the Posterior Distribution

The CLT tells us that our estimate of the spread \(\hat{d}\) has a normal distribution with expected value \(d\) and standard deviation \(\sigma\), which we calculated in a previous exercise.

Use the formulas for the posterior distribution to calculate the expected value of the posterior distribution if we set \(\mu = 0\) and \(\tau = 0.01\).

Instructions

100 XP
  • Define \(\mu\) and \(\tau\)
  • Identify which elements stored in the object results represent \(\sigma\) and \(Y\)
  • Estimate B using \(\sigma\) and \(\tau\)
  • Estimate the posterior distribution using B, \(\mu\), and \(Y\)