Module 1 — What is a Prior?

Contents

Module 1 — What is a Prior?#

Coming soon. This module will cover how prior distributions encode existing knowledge about temperature and proxy behaviour before any calibration data is introduced.

Preview#

A prior distribution is your best guess about a parameter before you look at the data. In TEXAS, priors are placed on the shape parameters of the calibration curve (t₀, k, b, v) and on observation noise.

Key ideas this module will cover:

  • What a probability distribution means for a physical parameter

  • How to read a prior plot: location, spread, and shape

  • The difference between informative and weakly informative priors

  • Why “I don’t know” is not the same as a flat prior

Contents