# Bayesian Model Selection

**URL:** <https://discourse.julialang.org/t/bayesian-model-selection/55725>\
**Category:** Statistics\
**Tags:** turing\
**Created:** [February 21, 2021, 4:34pm UTC](https://discourse.julialang.org/t/bayesian-model-selection/55725 "2021-02-21T16:34:11Z")\
**Posts on this page:** 1\
**Showing post:** 2

<div class="post-metadata">

**Author:** ![rikh](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/rikh/32/204104_2.png) [@rikh](https://discourse.julialang.org/u/rikh)\
**Post date:** [February 22, 2021, 9:15am UTC](https://discourse.julialang.org/t/bayesian-model-selection/55725/2 "2021-02-22T09:15:25Z")

</div>

> [@ANasc](#):
>
> How does one calculate Bayes factors, for example?

I don’t know much about the metrics for model selection, but do know that various statisticians argue against Bayes factors (Tendeiro & Kiers, [2019](https://doi.org/10.1037/met0000221); Gelman et al., [2020](https://doi.org/https://doi.org/10.1201/b16018)).

As far as I understand, McElreath ([2020](https://doi.org/10.1201/9780429029608)) and Gelman et al. ([2020](https://doi.org/https://doi.org/10.1201/b16018)) advise to do model comparison based on predictive performance measures such as cross-validation and AIC. McElreath ([2020](https://doi.org/10.1201/9780429029608)) argues that these metrics are quite similar.

But, I think that it also depends on where you want to publish. My co-authors aren’t comfortable with cross-validation, so I will stick to a multiverse analysis (Steegen et al., [2016](https://doi.org/10.1177%2F1745691616658637)) with a multilevel model to reduce overfitting and clear graphs to show what the model does.

---

_[View the full topic](https://discourse.julialang.org/t/bayesian-model-selection/55725)._