In Bayesian parameter estimation, uninformative priors are a way of making minimal assumptions about the model. They are commonly chosen to be invariant to certain transformations, such as translation or scaling. While uninformative priors are often improper, they can still lead to proper posterior distributions, and thereby be usable in posterior inference.
This concept has the prerequisites:
Core resources (we're sorry, we haven't finished tracking down resources for this concept yet)
Supplemental resources (the following are optional, but you may find them useful)
→ Pattern Recognition and Machine Learning
A textbook for a graduate machine learning course, with a focus on Bayesian methods.
Location: Section 2.4.3, pages 117-120
→ Mathematical Statistics and Data Analysis
An undergraduate statistics textbook.
Location: Section 8.6.1, "Further remarks on priors," pages 294-296
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