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Funny, I would consider many ML usages to be toy problems. Coming up with funny nonsensical Shakespeare plays or generating random Pantone colors or Pokémon nam

Funny, I would consider many ML usages to be toy problems. Coming up with funny nonsensical Shakespeare plays or generating random Pantone colors or Pokémon names isn’t something you hear from Bayesian systems. That’s the domain of ML. Recommending related products is again simple statistical analysis. And I can generate a prediction based on past performance real easy with some SQL and a couple of “if” statements. Just because Bayesian isn’t over-hyped doesn’t mean it doesn’t solve real problems. Not everything can be solved with ML, unless the problem is getting more investor money.

kahnjw 9y ago
I agree with all of this, except are you really implementing bayesian models in SQL? I'm not sure that is what the author is advocating in the first place. There are tools for bayesian everything, I much prefer using those over hacking together some illegible SQL script.
- freehunter 9y ago
  No, it’s just SQL statements pulling data into the if...then in your preferred language. I’ve never done actual logic with SQL, not even sure if it’s possible.
  - JetSpiegel 9y ago
    Each DB engine has it's own incompatible imperative stored procedure language. Having worked with Oracle's version, and seen entire systems buult out of that, I dread it.
peatmoss 9y ago
There are some methods that I don’t see used outside of the machine learning community, but there appears to be a fair degree of overlap in actual methods used between classical stats and machine learning. Now, the biggest difference that I can see is that, within machine learning contexts, people are more concerned with the quality of the predictions than the interpretation of the independent variables. That’s not hard and fast, but it seems to be a common thread. And for some problems, making a good prediction is really the right thing. In other cases, understanding the mechanisms you might use to effect an outcome is more important. Both are valid uses of statistical methods, depending on the problem. I think within the realm of classical statistics, Bayesian methods’ super power is in being able to generate results that are much easier to communicate to lay people. Also Bayesian methods are nice if you want to do sensitivity analysis in a principled up front kind of way. But I could imagine using those methods in an ML context even if they aren’t the current darlings of the methodological pantheon.

kahnjw 9y ago

I agree with all of this, except are you really implementing bayesian models in SQL? I'm not sure that is what the author is advocating in the first place. There are tools for bayesian everything, I much prefer using those over hacking together some illegible SQL script.

freehunter 9y ago
No, it’s just SQL statements pulling data into the if...then in your preferred language. I’ve never done actual logic with SQL, not even sure if it’s possible.
- JetSpiegel 9y ago
  Each DB engine has it's own incompatible imperative stored procedure language. Having worked with Oracle's version, and seen entire systems buult out of that, I dread it.

peatmoss 9y ago

There are some methods that I don’t see used outside of the machine learning community, but there appears to be a fair degree of overlap in actual methods used between classical stats and machine learning. Now, the biggest difference that I can see is that, within machine learning contexts, people are more concerned with the quality of the predictions than the interpretation of the independent variables. That’s not hard and fast, but it seems to be a common thread. And for some problems, making a good prediction is really the right thing. In other cases, understanding the mechanisms you might use to effect an outcome is more important. Both are valid uses of statistical methods, depending on the problem. I think within the realm of classical statistics, Bayesian methods’ super power is in being able to generate results that are much easier to communicate to lay people. Also Bayesian methods are nice if you want to do sensitivity analysis in a principled up front kind of way. But I could imagine using those methods in an ML context even if they aren’t the current darlings of the methodological pantheon.