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doc/objective-bayesian-inference.md

@@ -11,6 +11,8 @@ metrics such as frequentist matching coverage provide a way to quantify what it
 and how to build objective priors. Finally, it revists a few classical problems studied by Bayes and Laplace to
 show how they might be solved with a more modern approach to objective Bayesian inference.
 
+For more complete coverage of the topic, I highly recommend the book [Objective Bayesian Inference](https://www.amazon.com/Objective-Bayesian-Inference-James-Berger/dp/9811284903) by James Berger, Jose Bernardo, and Dongchu Sun.
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 1. [History](#history)
 2. [Priors and Frequentist Matching](#priors-and-frequentist-matching)
     * [Example 1: A Normal Distribution with Unknown Mean](#example-1-a-normal-distribution-with-unknown-mean)