But because you only have a finite number of bits, the takeaway for today is that there's only so much precision.

因为只有有限个比特，今天的重点就是，它就这么点精度。

哈佛公开课 - 计算机科学课程节选

That kind of problem tends to arise because they're using a language or a computer that only has finite precision and so there are rounding errors.

这类问题已经日趋严重了,由于他们使用的语言或电脑,都是精度有限的,因此存在这样的问题。

哈佛公开课 - 计算机科学课程节选

You make an observation doing an experiment that is as accurate as you can do.

得到了你能都,做到的最高精度。

麻省理工公开课 - 热力学与动力学课程节选

So this now is and up here is and let's put in numbers of quarters, so this will be, what will it be?

这一点是上面这点是,接下来我们把精度提高到1/4

耶鲁公开课 - 博弈论课程节选

So, a 32 bit of float gives you a bunch of precision.

2比特的浮点型数据精度更大。

哈佛公开课 - 计算机科学课程节选

So it turns out there's a solution to this: If you need more precision, more digits after the decimal point than a float allows, what do you go for instead?

所以这里有一个解决方案：,如果你需要比float类型允许的更高精度，小数点后更多位数,那你该用什么取代它？

哈佛公开课 - 计算机科学课程节选