On September 9th, the Reading Group discussed ‘The Bayesian Bootstrap’, by Donald B. Rubin. The abstract for the paper is below:
“The Bayesian bootstrap is the Bayesian analogue of the bootstrap. Instead of simulating the sampling distribution of a statistic estimating a parameter, the Bayesian bootstrap simulates the posterior distribution of the parameter; operationally and inferentially the methods are quite similar. Because both methods of drawing inferences are based on somewhat peculiar model assumptions and the resulting inferences are generally sensitive to these assumptions, neither method should be applied without some consideration of the reasonableness of these model assumptions. In this sense, neither method is a true bootstrap procedure yielding inferences unaided by external assumptions.”
Daniel presented the paper and his slides are available here.