# 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