Dissappointed. Also, I’ve seen that video linked somewhere else around here. Still interesting though.
Anyhow, the dichotomy he makes may work for some field/subfield—I don’t really know. But it doesn’t work for a lot of differences between perspectives on statistics.
Can you elaborate here at all? I feel bad for appealing to authority here, but Mike is widely considered the leader of the field of statistical ML, so it is a priori unlikely to me that his dichotomy is limited to a single subfield. It sounds like you think I should update away from his beliefs, and I would like to if he is indeed wrong, but you haven’t provided much evidence for me so far.
So Mike seems to be talking about (3) - whether to use “bayesian” or “frequentist” decision-making methods. However, the distinction I see (and use) most often is something like (2) - interpreting probabilities as reflecting a state of incomplete information (bayesian) or as reflecting a fact about the external world (frequentist).
Dissappointed. Also, I’ve seen that video linked somewhere else around here. Still interesting though.
Anyhow, the dichotomy he makes may work for some field/subfield—I don’t really know. But it doesn’t work for a lot of differences between perspectives on statistics.
Can you elaborate here at all? I feel bad for appealing to authority here, but Mike is widely considered the leader of the field of statistical ML, so it is a priori unlikely to me that his dichotomy is limited to a single subfield. It sounds like you think I should update away from his beliefs, and I would like to if he is indeed wrong, but you haven’t provided much evidence for me so far.
Fortunately, someone else has already done the work for me :)
http://lesswrong.com/r/discussion/lw/7ck/frequentist_vs_bayesian_breakdown_interpretation/
So Mike seems to be talking about (3) - whether to use “bayesian” or “frequentist” decision-making methods. However, the distinction I see (and use) most often is something like (2) - interpreting probabilities as reflecting a state of incomplete information (bayesian) or as reflecting a fact about the external world (frequentist).
Thanks.