id Url Resource template Resource class Title Creator Subject Description Publisher Contributor Date Type Format Identifier Language Relation Coverage Rights Alternative Title Abstract Date Created Date Issued Date Modified Medium Is Replaced By Replaces Requires Is Part Of Has Part Is Referenced By References Spatial Coverage Temporal Coverage Access Rights Bibliographic Citation License Rights Holder Cited by Cites Editor List of editors Status Doi Identifier Isbn Issn Issue Number of pages Number of volumes Pages Short title Uri Volume Name Surname Homepage Funded by Account Member Status Tag Number Current Location Is Shown At Is Shown By Europeana Rights Europeana Type Part of Is supported by Is supplied by Latitude Longitude Lat/long Has format Operating systems In series 10502 https://chloe.cnr.it/s/BiDiAr/item/10502 Academic Article bibo:AcademicArticle Getting Bayesian ideas across to a wide audience Cowgill, George 2002 eng https://creativecommons.org/licenses/by-nc-nd/4.0/deed.en CC BY-NC-ND 4.0 A generally Bayesian attitude toward statistical inference seems to me so obviously superior to the 'classical' Neyman-Pearson approach that it is difficult to comprehend why not everyone agrees. I believe that most non-statisticians learn classical procedures ritualistically but then interpret their results in naively Bayesian ways. It would be better if they became more sophisticated and knowing Bayesians. A truly introductory text on the logic of Bayesian inference, with some simple but useful applications, would probably help. Bayesian inference with an uninformative prior may yield the same results as classical inference, but with coherent rather than muddled logic. An example of a very useful but mathematically simple archaeological application of an informative prior is using prior information to improve estimates of true proportions of artifact categories in populations represented by small collections. However, a complication arises when the observed proportion in a fairly large sample is well outside the range considered at all likely for the relevant population, based on prior information. In this case, straightforward use of a beta prior distribution can yield results that seem unreasonable. Possibly our prior information is better represented by a modified beta distribution with 'heavy' tails. Advice about this problem would be appreciated. https://chloe.cnr.it/s/BiDiAr/item/2002 191–196 http://www.archcalc.cnr.it/journal/id.php?id=344 13 https://www.zotero.org/groups/5293298/bidiar/items/RHLWLA6E/item-list