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386 ENDNOTES77. See two famous editorials on the topic: Shannon (1956) and Elias (1958). Elias’ editorial is a clever workof satire and remains as current today as it was in 1958. Both of these one-page editorials are readily availableonline. [184]78. I really wish I could say there is an accessible introduction to maximum entropy, at the level of most naturaland social scientists’ math training. If there is, I haven’t found it yet. Jaynes (2003) is an essential source, but ifyour integral calculus is rusty, progress will be very slow. Better might be Steven Frank’s papers (2009; 2011)that explain the approach and relate it to common distributions in nature. You can mainly hum over the mathsin these and still get the major concepts. See also Harte (2011), for a textbook presentation of applications inecology. [186]79. Kullback and Leibler 1951. Note however that Kullback and Leibler did not name this measure aer themselves.See Kullback (1987) for Solomon Kullback’s reflections on the nomenclature. For what it’s worth, Kullbackand Leibler make it clear in their 1951 paper that Harold Jeffreys had used this measure already in the developmentof Bayesian statistics. [186]80. Akaike (1973). See also Akaike (1974, 1978, 1981), where AIC was further developed and related to Bayesianapproaches. Population biologists tend to know about AIC from Burnham and Anderson (2002), which stronglyadvocates its use. [190]81. Lunn et al. (2013) contains a fairly understandable presentation of DIC, including a number of different waysto compute it. [192]82. Watanabe (2010). [192]83. Gelman et al. (2013b) re-dub WAIC to give explicit credit to Watanabe, in the same way people renamedAIC aer Akaike. Gelman et al. (2013b) is worthwhile also for the broad perspective it takes on the inferenceproblem. [192]84. Stone (1977). [192]85. is is closely related to minimum description length. See Grünwald (2007). [192]86. Spiegelhalter et al. (2002). See Lunn et al. (2013) for an updated discussion. [196]87. Schwarz (1978). [196]88. Gelman and Rubin (1995). See also section 7.4, page 182, of Gelman et al. (2013a). [197]89. See Burnham and Anderson (2002) for a thorough argument in favor of this interpretation. e authors notethat some aspects of the procedure remain heuristic, because priors within models are le behind when usinginformation criteria, unlike when using Bayes factors. For some, that’s a virtue. For others, a sin. [206]90. Or alternatively the deviance of each model provides the likelihood and the penalty term provides the prior.See the presentation in Burnham and Anderson (2002). [206]91. See both Burnham and Anderson (2002) and Claeskens and Hjort (2008). [206]92. William Henry Harrison’s military history earned him the nickname “Old Tippecanoe.” Tippecanoe was thesight of a large battle between Native Americans and Harrison, in 1811. In popular opinion, he was a war hero.But in popular imagination, Harrison was cursed by the Native Americans in the aermath of the battle. [211]Chapter 793. All manatee facts here taken from Lightsey et al. (2006); Rommel et al. (2007). [215]94. Wald (1943). See Mangel and Samaniego (1984) for a more accessible presentation and historical context.[215]95. Wald (1950). Wald’s foundational paper is Wald (1939). Fienberg (2006) is a highly-recommended read forhistorical context. For more technical discussions, see Berger (1985), Robert (2007), and Jaynes 2003, page 406.