Month: May 2016

“There is, in fact, no way to represent any causal information in contingency tables (such as Tables 1.1 and 1.2), on which statistical inference is often based.  There are, however, extra-statistical methods that can be used to express and interpret causal assumptions. These… Continue Reading →

In a post by Kim Larsen, the interpretability advantage of Bayesian models is discussed with examples provided for using the bsts package in R: Sorry ARIMA, but I’m Going Bayesian