# Bayes factors with (overly) informative priors

## TL;DR

This paper examines the pitfalls of using overly informative priors with many independent parameters, demonstrating through examples and large sample theory how they hinder learning from data.

## Contribution

It provides a theoretical analysis of the issues caused by overly informative priors and illustrates these problems with practical examples.

## Key findings

- Overly informative priors can impede data learning.
- Large sample theory reveals the detrimental effects of such priors.
- Examples demonstrate the practical implications of the theoretical findings.

## Abstract

Priors in which a large number of parameters are specified to be independent are dangerous; they make it hard to learn from data. I present a couple of examples from the literature and work through a bit of large sample theory to show what happens.