Again, I don't have data to back this up, but just common sense suggests to me that the common case is if I'm on the subway platform I want to get on that train and maybe I want to get back and where are those two buttons, right?
此外,我没有数据来备份这个,但是常识告诉我,常见的情况是,如果我站在地铁站台上,我想乘上地铁,可能我还想回来,那么哪里能找到这两个按钮,对不?
So in fact, it is the case, if you look at data, and by the way, that's the way I ended up setting a lot of these parameters and playing with it, was comparing what my simulation said to historical stock data.
所以实际上,在这个例子中,如果你看到数据,顺便说一下,这就是我设置很多变量,然后编程的方式,也就是把我的仿真程序得到的结果,和历史股票数据进行比较。
You can take a problem that might be relatively intuitive to solve but when you scale this thing up as is increasingly the case in the web, in large data systems, and so forth, you actually have to now think smart, you actually have to think efficiently and you have to solve this problem effectively.
你可以把一个问题用比较直观的方法解决,但如果你把此类问题的数量增大,正如越来越多的互联网,和大规模数据系统中出现的问题等等,你应该考虑怎样才能更简便,怎样才能更高效,你应该用行之有效的方法处理问题。
In the case of Burt Malkiel's data, more than 11% per year and in the case of Roger Ibbotson's data between 7% and 8% per year of those returns can be explained either by backfill bias or survivorship bias.
在伯特·麦基尔的数据中,超过11%的年平均收益,在罗杰·伊博森的数据中,7%到8%的年平均收益,可以用生存偏差或回填偏差来解释
应用推荐