※ 引述《winniewo》之铭言： 我换个方式问好了 > 『Bayes estimator provide a tool for solving minimax problems. 贝氏估计量提供了一个解决minimax问题的工具 > Thus Bayesian considerations are helpful > when choosing an optimal frequentist estimator. 这句话看不大懂,什麽是"optimal frequentist estimator" > Viewed in this light, there is a > synthesis of the two approaches. 由此观点来看,就有一个两种方法的"synthesis" 什麽是 synthesis of the two approches. > The Bayesian approach provides us with a mean of > constructing an estimator that has > optimal frequentist properties. 贝式方法提供了我们 "with a mean of comstrcting" 估计量拥有 "optimal frequentist" 性质. > This synthesis highlights important features > of both the Bayesian and frequentist > approaches. 这个"sythesis"突显了贝式学与频率学重要的特性 > The Bayesian paradigm is > well suited for the construction of > possibly optimal estimators, but is > less well suited for their evaluation. 这句的意思不太了解 (1)什麽是construction of possibly optimal estimator (2)什麽是their evalustion (3)为什麽贝式适合的是(1)而较不适合(2) > The frequentist oaradigm is complementary, > as it is well suited for risk evaluations, > but less well suited for construction. 这个问题也跟上面一样...@@ > It is important to view these two > approaches and hence "Average risk > optimality" and "Minimaxity and admissibility" > as complementary rather than adversarial; > together they provide a rich set of tools > and techniques for the statisticaian. 这是个对於在看这两个方法是重要的, 因此"平均风险最佳性"与"大中取小性和允许性" 为互补的,而不是相互比较的,这两个都同时提供了 统计学者一个好的工具与方法. 这是在统计教本中讲贝氏估计量与 大中取小估计量的一段文章,我不是学 这方面的东西,but考试要考啊~我也很想看懂 -- 夫兵者不祥之器物或恶之故有道者不处君子居则贵左用兵则贵右兵者不祥之器非君子 之器不得已而用之恬淡为上胜而不美而美之者是乐杀人夫乐杀人者则不可得志於天下 矣吉事尚左凶事尚右偏将军居左上将军居右言以丧礼处之杀人之众以哀悲泣之战胜以 丧礼处之道常无名朴虽小天下莫能臣侯王若能守之万物将自宾天地相合以降甘露民莫 之令而自均始制有名名亦既有夫亦将知止知 220-133-64-250.HINET-IP.hinet.net海