Learning Outcomes Rework

[ericmjl]

Guidelines

• Feel free to modify this post to update the learning outcomes. This post is the "master document".
• Detailed discussion in the thread below; be sure to quote.

Format

This tutorial is split into 2 four-hour segments. The first segment deals with the basics of probability. The second deals with probabilistic programming and model formulation.

This is a very hands-on tutorial, including ample time for exploration and discovery.

Learning Outcomes

At the end of Part 1 of this tutorial, participants will be able to:

• Describe probability distributions by their "story".
• Identify cases where data can be modelled by a probability distribution.
• Describe a generative process for data, using probability distribution stories.
• TBC

At the end of Part 2 of this tutorial, participants will be able to:

• Use probability distribution diagrams to draw out a generative model diagrams for:
  • Parameter estimation models.
  • Group comparison models.
  • Hierarchical models.
  • Curve fitting models.
• Use PyMC3 syntax to implement the above generative models.
• Diagnose model appropriateness and fit using visual diagnostics.
• TBC.