How much of a Bayesian posterior distribution falls inside a region of practical equivalence (ROPE)

Articulo muy interesante sobre

How much of a Bayesian posterior distribution falls inside a region of practical equivalence (ROPE)

The posterior distribution of a parameter shows explicitly the relative credibility of the parameter values, given the data. But sometimes people want to make a yes/no decision about whether a particular parameter value is credible, such as the "null" values of 0.0 difference or 0.50 chance probability. For making decisions about null values, I advocate using a region of practical equivalence (ROPE) along with a posterior highest density interval(HDI). The null value is declared to be rejected if the (95%, say) HDI falls completely outside the ROPE, and the null value is declared to be accepted (for practical purposes) if the 95% HDI falls completely inside the ROPE. This decision rule accepts the null value only when the posterior estimate is precise enough to fall within a ROPE. The decision rule rejects the null only when the posterior exceeds the buffer provided by the ROPE, which protects against hyper-inflated false alarms in sequential testing. And the decision rule is intuitive: You can graph the posterior, its HDI, its ROPE, and literally see what it all means in terms of the meaningful parameter being estimated. (For more details about the decision rule, its predecessors in the literature, and alternative approaches, see the article on this linked web page.)

http://doingbayesiandataanalysis.blogspot.com/2013/08/how-much-of-bayesian-posterior.html