健壮性 Robustness

鲁棒是Robust的音译，也就是健壮和强壮的意思。它是在异常和危险情况下系统生存的关键。比如说，计算机软件在输入错误、磁盘故障、网络过载或有意攻击情况下，能否不死机、不崩溃，就是该软件的鲁棒性。所谓“鲁棒性”，是指控制系统在一定（结构，大小）的参数摄动下，维持其它某些性能的特性。根据对性能的不同定义，可分为稳定鲁棒性和性能鲁棒性。以闭环系统的鲁棒性作为目标设计得到的固定控制器称为鲁棒控制器。

计算机科学中，健壮性（英语：Robustness）是指一个计算机系统在执行过程中处理错误，以及算法在遭遇输入、运算等异常时继续正常运行的能力。 诸如模糊测试）之类的形式化方法中，必须通过制造错误的或不可预期的输入来验证程序的健壮性。很多商业产品都可用来测试软件系统的健壮性。健壮性也是失效评定分析中的一个方面。

这是我在PRML上截的一段话，说T分布比高斯分布有更好的鲁棒性

        we see that Student’s t-distribution is obtained by adding up an infinite number of Gaussian distributions having the same mean but different preci-sions. This can be interpreted as an infinite mixture of Gaussians (Gaussian mixtures will be discussed in detail in Section 2.3.9. The result is a distribution that in gen-eral has longer ‘tails’ than a Gaussian, as was seen in Figure 2.15. This gives the t-distribution an important property calledrobustness, which means that it is much less sensitive than the Gaussian to the presence of a few data points which are outliers. The robustness of the t-distribution is illustrated in Figure 2.16, which compares the maximum likelihood solutions for a Gaussian and a t-distribution. Note that the max-imum likelihood solution for the t-distribution can be found using the expectation-maximization (EM) algorithm. Here we see that the effect of a small number of  Figure 2.16 Illustration of the robustness of Student’s t-distribution compared to a Gaussian. (a) Histogram distribution of 30 data points drawn from a Gaussian distribution, together with the maximum likelihood fit ob-tained from a t-distribution (red curve) and a Gaussian (green curve, largely hidden by the red curve). Because the t-distribution contains the Gaussian as a special case it gives almost the same solution as the Gaussian. (b) The same data set but with three additional outlying data points showing how the Gaussian (green curve) is strongly distorted by the outliers, whereas the t-distribution (red curve) is relatively unaffected. outliers is much less significant for the t-distribution than for the Gaussian. Outliers can arise in practical applications either because the process that generates the data corresponds to a distribution having a heavy tail or simply through mislabelled data. Robustness is also an important property for regression problems. Unsurprisingly, the least squares approach to regression does not exhibit robustness, because it cor-responds to maximum likelihood under a (conditional) Gaussian distribution. By basing a regression model on a heavy-tailed distribution such as a t-distribution, we obtain a more robust model.