Hello Kevin,

Well for starters, you could try the marine turtle newsletter(!)

http://mgel2011-kvm.env.duke.edu/wp-content/publicuploads/eguchi-2008-intro-to-baysian-statistics.pdf

It seems an unlikely start point, but it does do a good job with references to standard texts, and it is a fairly readable introduction to the topic.

That it comes from a conservation oriented source is not surprising. Bayesian approaches make sense in a conservation context, where there is a stream of data that is used to update prior probabilities to posterior probabilities, often the case in fisheries management and environmental monitoring.

Bayesians like Bayes (usually never having read Bayes 1763). And experimentalists are not soon likely to give up calculating sample sizes based on frequentist statistics (a necessary requirement for funding in medical research). So before putting in the necessary effort to learn and use Bayesian stats I'm going to recommend that you read the first chapter of Royall (1997) Statistical Evidence. A Likelihood Paradigm. I recommend that you think about and reflect on what Royall has to say, as it applies to your research interests.

~David S

Here are good books:

McElreath, Statistical Rethinking (https://www.amazon.de/Statistical-Rethinking-Bayesian-Examples-Chapman/dp/1482253445)

Kruschke, Doing Bayesian Data Analysis (https://www.amazon.de/Doing-Bayesian-Data-Analysis-Kruschke/dp/0124058884)

Gelman et al., Bayesian Data Analysis (https://www.amazon.de/Bayesian-Analysis-Chapman-Statistical-Science/dp/1439840954)

Slides are a lot around the web, I find these useful, e.g.:

https://www.tjmahr.com/bayes-intro-lecture-slides-2017/

https://m-clark.github.io/docs/IntroBayes.html

And then I find the vignettes from the rstanarm-package really helpful:

https://cran.r-project.org/package=rstanarm

Thank you both! Looks like some really good places to start.