My name is Chris Elrod. I am currently a PhD student at Baylor University. I am a flamboyantly flaming Bayesian, who things it is a real tragedy that we’re not able to apply Bayes to all our statistics and inference problems; Bayesian computation is often just too darn intractable. So my focus has been on research and implementing ways we can speed it and make it more accessible.
While I would love to turn to large problems with over a thousand parameters or more, my focus so far has been on models with closer to 5-10 parameters. The application so far has been in sample size determination and power analysis, where we refit the model thousands of times. Another potential application is adaptive experimental designs, where researches would like to rerun simulations prior to each decision.
All code in my posts is free under the MIT liscense, and the rest of their content under the Creative Commons.