Useful Bayesian explanations

I’ve been going over Bayesian analysis principals recently and trying to work out ways to explain it clearly (and  teach it).

Here are are my top 5 links that do a reasonable job at explaining Bayesian principles (in no particular order):

  1. Count Baysie with Lego: https://www.countbayesie.com/blog/2015/2/18/bayes-theorem-with-lego
  2. Julia Galef has some good explantions: https://www.youtube.com/watch?v=BrK7X_XlGB8
  3. Aaron Eliison’s paper on Bayesian inference in ecology : http://www.uvm.edu/~bbeckage/Teaching/DataAnalysis/AssignedPapers/Ellison_2004.pdf
  4. The ETZ files Bayes theorem.
  5. On a lighter note you can’t beat xkcd:

Frequentists vs. Bayesians

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Coupling BEAST with eco-phylogenetics: Tales from Glascow

Can community level phylogenetics be used effectively with population genetic approaches to better understand infectious disease dynamics? This was one of the questions that came up on my recent trip to the University of Glascow. It was great fun hanging out with the fine folk from the IBAHCM (Institute of Biodiversity Animal Health and Comparative Medicine) with numerous discussions about life, the world and all sorts of disease ecology topics. The purpose of the trip was a research exchange with Roman Biek and is lab to become more familiar with BEAST and associated phylodynamic tools. Naturally it got me thinking about how to synthesize these tools with community phylogenetics – particularly in understanding transmission dynamics. Basically BEAST provides excellent spatial/temporal estimations of disease spread, but is not as good at linking phylogenetic information to multiple interacting landscape and host variables. They are my conclusions for now anyway – BEAST can do GLMs apparently but I’ve heard the interpretation can be difficult.Stay tuned for  my review on the topic which is nearly ready to submit somewhere.

On a more applied note – if you are a BEAST user or interested in becoming one here is a link to a useful set of tutorials: https://github.com/beast-dev/beast-mcmc/tree/master/doc/tutorial. Also I can highly recommend the R package ‘Seraphim’ for post-BEAST spatial analysis of pathogen dynamics: http://evolve.zoo.ox.ac.uk/Evolve/Seraphim.html – though installing in R is a little tricky (this will be a topic of a future blog post).