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贝叶斯生命表分析AI 解读

Summary The moments of the posterior p.d.f. of the probability of surviving to time t, P(t), are obtained, assuming a time-dependent Poisson process for failures and i.i.d. gamma prior densities for the hazard function. For improper densities the results reduce to the Kaplan–Meier estimate of p(t) and to the Cox result for the comparison of two survival curves, which, because of the use of improper priors, may be inadmissible with respect to mean square error loss. A way of…

若干经典假设的贝叶斯检验及其序贯临床试验应用AI 解读

Consider someone who sets out to collect data sequentially in such a way as to disprove a hypothesis, H 0, about the value of θ, the mean of a normal distribution. Observation is continued as long as the posterior probability of H 0 exceeds α1 and stopped when it falls below it. It is shown that if a non-zero prior probability, p, is assigned to H 0(0≤α1 < p) and the remaining prior probability is spread over alternate values of θ that the probability that H 0 will…

多元正态分布参数的后验分布AI 解读

SUMMARY A class of Bayes posterior distributions is obtained for the parameters of the multivariate normal distribution. These are compared with their fiducial and confidence counterparts.

极大似然法计算剂量反应曲线的若干新方面AI 解读

The estimation of the parameters of dosage response curves by the standard probit method is an iterative process beginning with approximations to the parameters and using one or more cycles of computations to “improve” these estimates until they converge. The present paper gives a table and method for computing the maximum likelihood solution which converges more rapidly than the standard probit method. A procedure is presented for obtaining more accurate initial…