Exchangeability martingales can be used to find strange or odd data points. The mechanics of exchangeability martingales is still a bit of a mystery to me. However, posted below are the basic components and how each step gets us to the desired result of an exchangeability martingale.
The IKEA Stockholm mirror has 2 little eyelets on the back off to the left and right. One way to hang the mirror is to measure the distance between the eyelets and put nails/screws precisely lined up with the eyelets. This seems like a recipe for lots of mistakes. I found another solution.
After using Windows Explorer to navigate to a directory, I just want to copy and paste the directory in R, but I get this error:
> setwd("C:\Program Files") Error: '\P' is an unrecognized escape in character string starting ""C:\P"
I used the setwd() function and pasted the directory, but there is an answer!
Where do I get started with data-driven engineering? How can the 3 I’s of data-driven engineering help me get off to a running start? How can I avoid the common pitfalls of data-driven engineering?
A system can be under both stress and strain, but how do they differ? How are they related? Also, why make the distinction?
Language and semantics around system faults are important. For example, what’s so important about differentiating between blocking and masking faults?
In Weinberg’s Why Software Gets In Trouble, he discusses the difference between failures and faults. Although an obviously old concept, I’m finding it to be a novel approach to thinking about these issues (maybe novel to just me).
What is a system, systems thinking, science, philosophy, theory? How are they related? How can they be used?
Is software testing just checking if software conforms to the requirements? Sometimes… but usually the answer is no.