Reliability Quotes

“Simplicity is prerequisite for reliability.”

“In the short run, technology many be more efficient than man, but it will never be perfect. Every piece of equipment will eventually reveal an error code. In the long run, man will never be perfect, but prove to be more reliable than technology.”

“Ethics is the key which opens big doors to business success. However once it gets lost, the same access is locked for ever. The newly acquired access gets eclipsed with lost reliability.”

“I shed a tear when I meet somebody who always quits. Reliable people are so rare in this world.”

“ Home is not a place. Home is security, predictability, reliability, dependability, safety, permanence combined together. ”

“By the end of the 1950s, American cars were so reliable that their reliability went without saying even in car ads. Thousands of them bear testimony to this today, still running on the roads of Cuba though fueled with nationalized Venezuelan gasoline and maintained with spit and haywire.”

“Tedious as it may appear to some to dwell on the discovery of odds and ends that have, no doubt, been thrown away by the owner as rubbish ... yet it is by the study of such trivial details that Archaeology is mainly dependent for determining the date of earthworks. ... Next to coins fragments of pottery afford the most reliable of all evidence ...”

“I am not angry at Microsoft, as they did give me Windows 10 for free! I do feel a little misled about its reliability on older computers never certified for its installation by the manufacturer though.”

“Due to the various pragmatic obstacles, it is rare for a mission-critical analysis to be done in the “fully Bayesian” manner, i.e., without the use of tried-and-true frequentist tools at the various stages. Philosophy and beauty aside, the reliability and efficiency of the underlying computations required by the Bayesian framework are the main practical issues. A central technical issue at the heart of this is that it is much easier to do optimization (reliably and efficiently) in high dimensions than it is to do integration in high dimensions. Thus the workhorse machine learning methods, while there are ongoing efforts to adapt them to Bayesian framework, are almost all rooted in frequentist methods. A work-around is to perform MAP inference, which is optimization based.Most users of Bayesian estimation methods, in practice, are likely to use a mix of Bayesian and frequentist tools. The reverse is also true—frequentist data analysts, even if they stay formally within the frequentist framework, are often influenced by “Bayesian thinking,” referring to “priors” and “posteriors.” The most advisable position is probably to know both paradigms well, in order to make informed judgments about which tools to apply in which situations.”