22 Bayesian Estimation via Gibbs Sampling

Learning Objectives

By the end of this chapter, you will be able to:

State the Bayesian formulation of the animal model and its priors
Derive the full conditional distributions
Implement a Gibbs sampler for a single-trait animal model
Assess convergence and mixing
Summarize posteriors and compare them to REML point estimates

Assumes Ch 21.

Chapter in progress

This chapter has not been written yet. The objectives above are the contract it will be written against — they are taken from CHAPTERS.md, which is the outline for the whole book.

Purpose. The third route to the same variance components, and the one that gives full distributions.

---
title: "Bayesian Estimation via Gibbs Sampling"
---

::: {.callout-note icon=false}
## Learning Objectives

By the end of this chapter, you will be able to:

1. **State** the Bayesian formulation of the animal model and its priors
2. **Derive** the full conditional distributions
3. **Implement** a Gibbs sampler for a single-trait animal model
4. **Assess** convergence and mixing
5. **Summarize** posteriors and compare them to REML point estimates
:::

*Assumes Ch 21.*

::: {.callout-warning}
## Chapter in progress

This chapter has not been written yet. The objectives above are the contract it will be written
against — they are taken from `CHAPTERS.md`, which is the outline for the whole book.

**Purpose.** The third route to the same variance components, and the one that gives full distributions.
:::