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3. I agree with your point in some instances - but in others there is a huge risk of building a complicated model that everybody trusts that is just a pile of t

3. I agree with your point in some instances - but in others there is a huge risk of building a complicated model that everybody trusts that is just a pile of twaddle; in which case the expert judgement would better stay in the experts' heads, or on the odd bar-chart. I have not noted this point in many statistics courses, but it is all over the actuarial courses. 1. And so the journal reviewers shouldn't be. They are interested in the results, and act like the financial or insurance regulator in making sure those results have been calculated in an unbiased manner. 4. Yes, choice of model introduces subjectivity. That two beers a day are alright doesn't imply I should have ten. p.s. Does it not start life as an 'attempt at rebuttal'? 'Distinctly practical and unscientific'?, 'ridiculous'? I know it's the internet - but...

tel 16y ago
Apologies there, I was reacting to the exaggerated choice in anecdote, but actually meant the "practical" line favorably. Science and practicality can be at ends, but at the end, practicality wins by definition, right? 3. I agree that complex model building is fertile ground for hand-waving, this-is-too-complex-to-understand-so-just-accept-it false justification. I don't think large models are endemic to either Bayesian or Frequentist methods, though Bayesian methods do allow them to be built more easily. In both cases, I think simple sanity checks form a foundation that is necessary for the presentation of complex models. 1. I think the idea of being "interested in results" is a false hope. Experimentation does not always produce results, and when it does you probably don't even need statistics because the results are so obvious. In all other cases, uncertainty has become a major factor and demanding clarity is foolish. Tempting, but foolish. 4. I think under-modeling is dangerous, too. At the very least, it can rob you of the ability to quantify educated conclusions short of doing meta-analysis. At least with priors you're telling everyone you're a bit tipsy instead of stamping a bar chart down and having people swallow assumptions under the stamp of 'objectivity'. It's kind of like Python 'import this'. Explicit is better.
- hessenwolf 16y ago
  Mcmc is what takes the time more tham the size of the model. Definitely large models seem more likely to me in bayesian because it is so much neater to build them. I notice that you are very fervent in your bayesianism, but please correct me if i read that wrongly. I was once at a conference in which a similar debate was ongoing at the lectern. Dr cox was main guest of the conf and when he was asked where he stood he said, and i misquote probably awfully from poor memory, that it was a bit silly arguing about it because you just used whichever was appropriate to the task at hand. I thought that was pretty cool.
  - tel 16y ago
    I like Bayesianism because I think it's mathematically and philosophically cleaner. Then again, I think MCMC seems to show that it isn't so computationally clean. In realtime systems I am more than happy to use Frequentist models for their speed. I suppose believe in a world where Bayesian methods are the primary didactic statistics useful in sciences and communication and Frequentist methods are used when closed form estimators are necessary and coherent with Bayesian estimates. Or maybe an even better world where we have closed form, useful estimates from both camps.
    - hessenwolf 16y ago
      Just think quantum computing. You could write down everything you know and every piece of data you have, eliciting by hand your utility and priors, and then press go on the mcmc. Awesome.

tel 16y ago

Apologies there, I was reacting to the exaggerated choice in anecdote, but actually meant the "practical" line favorably. Science and practicality can be at ends, but at the end, practicality wins by definition, right? 3. I agree that complex model building is fertile ground for hand-waving, this-is-too-complex-to-understand-so-just-accept-it false justification. I don't think large models are endemic to either Bayesian or Frequentist methods, though Bayesian methods do allow them to be built more easily. In both cases, I think simple sanity checks form a foundation that is necessary for the presentation of complex models. 1. I think the idea of being "interested in results" is a false hope. Experimentation does not always produce results, and when it does you probably don't even need statistics because the results are so obvious. In all other cases, uncertainty has become a major factor and demanding clarity is foolish. Tempting, but foolish. 4. I think under-modeling is dangerous, too. At the very least, it can rob you of the ability to quantify educated conclusions short of doing meta-analysis. At least with priors you're telling everyone you're a bit tipsy instead of stamping a bar chart down and having people swallow assumptions under the stamp of 'objectivity'. It's kind of like Python 'import this'. Explicit is better.

hessenwolf 16y ago
Mcmc is what takes the time more tham the size of the model. Definitely large models seem more likely to me in bayesian because it is so much neater to build them. I notice that you are very fervent in your bayesianism, but please correct me if i read that wrongly. I was once at a conference in which a similar debate was ongoing at the lectern. Dr cox was main guest of the conf and when he was asked where he stood he said, and i misquote probably awfully from poor memory, that it was a bit silly arguing about it because you just used whichever was appropriate to the task at hand. I thought that was pretty cool.
- tel 16y ago
  I like Bayesianism because I think it's mathematically and philosophically cleaner. Then again, I think MCMC seems to show that it isn't so computationally clean. In realtime systems I am more than happy to use Frequentist models for their speed. I suppose believe in a world where Bayesian methods are the primary didactic statistics useful in sciences and communication and Frequentist methods are used when closed form estimators are necessary and coherent with Bayesian estimates. Or maybe an even better world where we have closed form, useful estimates from both camps.
  - hessenwolf 16y ago
    Just think quantum computing. You could write down everything you know and every piece of data you have, eliciting by hand your utility and priors, and then press go on the mcmc. Awesome.