This is probably very arrogant of me to say, but my advice would be: “Listen to the domain expert when he tells you what you should do… and then find a Bayesian and let them explain to you why that works.”
In my defense, this was my personal experience with statistics at school. I was very good at math in general, but statistics somehow didn’t “click”. I always had this feeling as if what was explained was built on some implicit assumptions that no one ever mentioned explicitly, so unlike with the rest of the math, I had no other choice here but to memorize that in a situation x you should do y, because, uhm, that’s what my teachers told me to do. -- More than ten years later, I read LW, and here I am told that yes, the statistics that I was taught does have implicit assumptions, and suddenly it all makes sense. And it makes me very angry that no one told me this stuff at school. -- I am a “deep learner” (this, not this), and I have problem learning something when I am told how, but I can’t find out why. Most people probably don’t have a problem with this, they are told how, and they do, and can be quite successful with it; and probably later they will also get an idea of why. But I need to understand the stuff from the very beginning, otherwise I can’t do it well. Telling me to trust a domain expert does not help; I may put a big confidence in how, but I still don’t know why.
ChristianKI is not telling you to trust a domain expert, but rather to read / listen to the domain expert long enough to understand what they are saying (rather than instantly assuming they are wrong because they say something that seems to conflict with your preconceived notions).
I think if you were to read most machine learning books, you would get quite a lot of “why”. See this manuscript for instance. I don’t really see why you think that Bayesians have a monopoly on being able to explain things.
I think you make a mistake if you put a school teacher who doesn’t understand statistics on a deep level into the same category of academic machine learning experts who don’t happen to be “Bayesians”.
This is probably very arrogant of me to say, but my advice would be: “Listen to the domain expert when he tells you what you should do… and then find a Bayesian and let them explain to you why that works.”
In my defense, this was my personal experience with statistics at school. I was very good at math in general, but statistics somehow didn’t “click”. I always had this feeling as if what was explained was built on some implicit assumptions that no one ever mentioned explicitly, so unlike with the rest of the math, I had no other choice here but to memorize that in a situation x you should do y, because, uhm, that’s what my teachers told me to do. -- More than ten years later, I read LW, and here I am told that yes, the statistics that I was taught does have implicit assumptions, and suddenly it all makes sense. And it makes me very angry that no one told me this stuff at school. -- I am a “deep learner” (this, not this), and I have problem learning something when I am told how, but I can’t find out why. Most people probably don’t have a problem with this, they are told how, and they do, and can be quite successful with it; and probably later they will also get an idea of why. But I need to understand the stuff from the very beginning, otherwise I can’t do it well. Telling me to trust a domain expert does not help; I may put a big confidence in how, but I still don’t know why.
ChristianKI is not telling you to trust a domain expert, but rather to read / listen to the domain expert long enough to understand what they are saying (rather than instantly assuming they are wrong because they say something that seems to conflict with your preconceived notions).
I think if you were to read most machine learning books, you would get quite a lot of “why”. See this manuscript for instance. I don’t really see why you think that Bayesians have a monopoly on being able to explain things.
I think you make a mistake if you put a school teacher who doesn’t understand statistics on a deep level into the same category of academic machine learning experts who don’t happen to be “Bayesians”.