ABSTRACT
The more vague the prior information, the greater the prior variability. The priors of the optimist and the clueless represent these two extremes. An informative prior reflects specific information about the unknown variable with high certainty, i.e., low variability. A vague or diffuse prior reflects little specific information about the unknown variable. A flat prior, which assigns equal prior plausibility to all possible values of the variable, is a special case. The bayesrules package includes a partial version of this dataset, named bechdel. The implications of these results are mathemagical. In general, consider what happens to the posterior mean as people collect more and more data. The rate at which this drift occurs depends upon whether the prior tuning is informative or vague. The phrase “as more and more data come in” evokes the idea that data collection, and thus the evolution in our posterior understanding, happens incrementally.
