By Emery N. Brown, Adam Sapirstein (auth.), Constantine Gatsonis, James S. Hodges, Robert E. Kass, Nozer D. Singpurwalla (eds.)
Like its predecessor, this moment quantity offers unique purposes of Bayesian statistical research, each one of which emphasizes the medical context of the issues it makes an attempt to unravel. The emphasis of this quantity is on biomedical purposes. those papers have been offered at a workshop at Carnegie-Mellon college in 1993.
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Additional resources for Case Studies in Bayesian Statistics, Volume II
1988). Microsphere estimates of blood flow: methodological considerations. American Journal of Physiology 254, G275-G279. Weisberg, L. C. thy in humans: Role of renal vasoconstriction. Kidney International 41, 1408-1415. , Dudek, R. J. (1991). The effect of lysophosphatidylcholine on coronary and renal circulation in the rabbit. Lipids 26, 223-226. , Hirano, T. and Tobe, T. (1993). Role of pancreatic blood flow and vasoactive substances in the development of canine acute pancreatitis. Journal of Surgical Research 55, 531-36.
Actually, it does not make sense to talk about a single distribution whose variance tracks the mean, since variance-mean links are properties of families of distributions, not a single distribution. The best distribution is hard to determine, but should include very long tails to account for minor disasters - major disasters hopefully will be thrown out. Section 4 below gives a calculation that shows that the behavior of the first few moments of the microsphere counting distribution does not depend on the distribution chosen.
As noted, this analysis can easily be extended to include other sources of uncertainty. , 1991). We suspect that the differences between the blood flow estimates obtained from these microspheres and the white ones may reflect a problem in counting the latter in the reference sample. These microspheres are at times difficult to distinguish from the background and cellular debris. As we stated earlier, these differences could also represent actual physiologic changes in the state of the animal for a variety of reasons.
Case Studies in Bayesian Statistics, Volume II by Emery N. Brown, Adam Sapirstein (auth.), Constantine Gatsonis, James S. Hodges, Robert E. Kass, Nozer D. Singpurwalla (eds.)