In a Bayes change-detection problem, a prior distribution is available for the change time.
In the early 1990s, Bronk Ramsey became interested in the application of Bayesian statistics to the analysis of radiocarbon data.
In Bayesian statistics, the log-Cauchy distribution can be used to approximate the improper Jeffreys-Haldane density, 1/k, which is sometimes suggested as the prior distribution for k where k is a positive parameter being estimated.
In Bayesian statistics, this can be modelled by using a prior distribution for one's assumptions on the fairness of the coin, then Bayesian inference to update this distribution.
Alternatively, in Bayesian terms, a prediction interval can be described as a credible interval for the variable itself, rather than for a parameter of the distribution thereof.
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Seymour Geisser, a proponent of predictive inference, gives predictive applications of Bayesian statistics.
Quasi-likelihood has no role in Bayesian statistics, as this is based on a fully specified probability model for the data.
The scaled inverse chi-squared distribution also has a particular use in Bayesian statistics, somewhat unrelated to its use as a predictive distribution for x = 1/s2.
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Michael A. Bailey used a slightly different Markov chain Monte Carlo Bayesian method to determine ideological leanings and made significantly different scaling assumptions.
R v Adams 1996 2 Cr App R 467, 1996 Crim LR 898, CA and R v Adams 1998 1 Cr App R 377, The Times, 3 November 1997, CA, are rulings that ousted explicit Bayesian statistics from the reasoning admissible before a jury in DNA cases.