Bayesian methods are an increasingly popular form of statistical analysis which uses informative prior distributions to help calculate posterior distributions of models that represent different hypotheses. Frequentist methods are contrasting methods that are used more commonly and more well known, but have come under recent criticism. I examined data gathered by Ellen Robertson, who used information theoretic methods for a Masters’ Thesis in Ecology and Environmental Science at the University of Maine to analyze the daily survival probabilities of marsh birds with a Bayesian perspective in order to get a sense of the Bayesian analysis. Results were as expected; when using uninformative prior distributions, the Bayesian analysis had almost the same results as Robertson’s. With the use of Robertson’s calculated parameter estimates as informative prior distributions, the Bayesian analysis still ended with similar results. The conclusions in all three versions of statistical analysis were the same. Hence, Bayesian methods can construct models representing hypotheses effectively.
Hardy, Sean, "Bayesian Analysis of Data on Nest Success for Marsh Birds" (2013). Honors College. 128.