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1. Your list of references proves that Bayesian statisticians have been writing papers across a variety of disciplines. 2. Bayesian methods are not readily com

1. Your list of references proves that Bayesian statisticians have been writing papers across a variety of disciplines. 2. Bayesian methods are not readily computed with today's hardware and software, and my desk is a counter-example. 2.1 Last time I fitted a Bayesian model, I had 600 processors with infinibandy things joining them up and left it for a week. 2.2 None of the software I use does Bayesian by default. 3. In practice, a lot of statistics in industry can be done by barcharts. I know that that is hard to hear. It is a big leap from there to Bayesian; bigger by far than from there to chi-sq tests. Data are so sparse, expert judgement so rich, and time so short... 4. Priors introduce subjectivity - no doubt about it. However, so do utility functions, pretty much cancelling it in my opinion. It is inappropriate to use p-values as a decision making framework for various reasons, but a lot of scientific papers are about recording experimental observations, not decision-making. Policy-decisions should use utility functions and priors; but I am happy with my science papers frequentist.

wisty 16y ago
5. Bayesian analysis is too easy. You don't have to transform the data in any convoluted way, you just describe your model and crank the handle. Publishers will no longer be able to sort the sheep from the goats.
- hessenwolf 16y ago
  Ha ha. Yes, it is a bit like that. If somebody could just replace that pesky mcmc convergence crap with something that worked, or give me a quantum computer, then I would convert my models Bayesian overnight.
lazyjeff 16y ago
I agree with the general theme of the OP. Bayesian methods are technically better than frequentist methods. However, like Betamax vs. VHS, there is more than just the technical correctness. Your point in 2.2 is the big one -- if there was a simple way to switch to Bayesian methods in existing statistics software like SPSS, that would be quite revolutionary. Right now, null hypothesis testing is too easy to do and widely accepted, even though the results may be completely wrong.
nazgulnarsil 16y ago
priors acknowledge subjectivity.
- hessenwolf 16y ago
  Also, this is true, but I think it doesn't disagree with my point. I just dread the length of my Own Risk and Solvency Assessment (ORSA) after Solvency II (the regulations for insurance companies in Europe) takes hold next year, if I have to explain the origins of my priors. I actually think we could do some fuck-awesome work in evaluating our risk capital requirements using priors on all of of our inputs, and the computational requirements would not be 'that' frightening, given the valuations are all Monte Carlo anyway. It's not happening, yet, though.
- Sniffnoy 16y ago
  I think about this point someone should link to [share likelihood ratios, not posterior beliefs](http://www.overcomingbias.com/2009/02/share-likelihood-ratios-not-posterior-beliefs.html http://www.overcomingbias.com/2009/02/share-likelihood-ratio...)... the summary is that you can do your Bayesian analysis without specifying the prior and just report the resulting likelihood ratios, telling everyone, "Here are the likelihood ratios, update your beliefs appropriately." Though that may lack some practicality.
noelwelsh 16y ago
Counterpoint: Last time I fitted a Bayesian model, I had 1 processor with some interpreted code and left it for ten minutes. YMMV.
- bluekeybox 16y ago
  Absolutely. Not all applications of Bayesian analysis are computationally-intensive. In some cases (an example: finding of single-nucleotide polymorphisms in next-gen sequencing data), Bayesian analysis comes down to multiplying prior probability of a SNP (for humans, 0.001 per genome position) by a few other numbers from the data itself to obtain posterior probability, which can be done in a linear time in a few minutes on tens of gigabytes of NGS data. And the best part is, no Bonferroni adjustment bullshit!
  - hessenwolf 16y ago
    Hey, don't forget the Benjamini-Hochberg replacement for Bonferroni. The situation is not 'that' bad.
tel 16y ago
A point by point rebuttal. 1. The op knows this but is implicitly believing his audience is different from the cited fields often enough to make point (1) relevant. Moreover, it's perfectly correct to say that many journal reviewers are not interested in Bayesian methods. 2.1. This is highly anecdotal and not at all a strong point. I'm sure Efron has knocked out a 600 core cluster doing frequentist bootstrapping (1). I'm also certain that many, many Bayesian methods run near instantly on modern hardware. I'll concede that there are fewer closed form results, though. 2.2 This is very true. Entrepreneurs? 3. "Data so sparse, expert judgement so rich" is exactly where Bayesian analysis is most pertinent. Use a prior to clarify and quantify your expert opinion and then demonstrate that indeed your few observations are worthy to change someone's opinion. 4. Choice of frequentist testing regime introduces subjectivity, too! Moreover, since these have been heralded as "objective" for so long it's pretty difficult to get people to recognize as much. Oftentimes, a frequentist method will be equivalent to a Bayesian method under a maximally uninformed prior. This is still a subjective assumption (though there are benefits of such a prior). --- Frequentists test are oftentimes very necessary. They have already been highly optimized in many cases and thus are available on low resource computing platforms. They are definitely an important engineering solution! That said, Bayesian methods do a far better job being clear in their assumptions and simple in their logic. There is certainly room for better software (free or otherwise) to replace BUGS/JAGS/whatever for the largest use cases of statistics in many fields. Also, another point you make about Bayesian methods making life difficult during certification and publication is exactly right, and probably the largest (unspoken) reason why they're not going to be used in core scientific fields for a long while. But both of those reasons are distinctly practical and unscientific. Bayesian methods do a better job using your data. They do this by allowing expert knowledge to enter into statistics in a sensible fashion. Finally, they introduce an easily understood interpretation on the answers to your statistical questions. You might not personally want to use them today for practical reasons, but the author of this article is very much in the right to try to encourage more scientists in more fields to take a look. (1) Sorry, I'm actually not at all sure if this is the case. Bootstrapping is still more computationally efficient than MCMC, I think. I just used the example because I think it's ridiculous to make either point.
- hessenwolf 16y ago

wisty 16y ago

5. Bayesian analysis is too easy. You don't have to transform the data in any convoluted way, you just describe your model and crank the handle. Publishers will no longer be able to sort the sheep from the goats.

hessenwolf 16y ago
Ha ha. Yes, it is a bit like that. If somebody could just replace that pesky mcmc convergence crap with something that worked, or give me a quantum computer, then I would convert my models Bayesian overnight.

lazyjeff 16y ago

I agree with the general theme of the OP. Bayesian methods are technically better than frequentist methods. However, like Betamax vs. VHS, there is more than just the technical correctness. Your point in 2.2 is the big one -- if there was a simple way to switch to Bayesian methods in existing statistics software like SPSS, that would be quite revolutionary. Right now, null hypothesis testing is too easy to do and widely accepted, even though the results may be completely wrong.

nazgulnarsil 16y ago

priors acknowledge subjectivity.

hessenwolf 16y ago
Also, this is true, but I think it doesn't disagree with my point. I just dread the length of my Own Risk and Solvency Assessment (ORSA) after Solvency II (the regulations for insurance companies in Europe) takes hold next year, if I have to explain the origins of my priors. I actually think we could do some fuck-awesome work in evaluating our risk capital requirements using priors on all of of our inputs, and the computational requirements would not be 'that' frightening, given the valuations are all Monte Carlo anyway. It's not happening, yet, though.
Sniffnoy 16y ago
I think about this point someone should link to [share likelihood ratios, not posterior beliefs](http://www.overcomingbias.com/2009/02/share-likelihood-ratios-not-posterior-beliefs.html http://www.overcomingbias.com/2009/02/share-likelihood-ratio...)... the summary is that you can do your Bayesian analysis without specifying the prior and just report the resulting likelihood ratios, telling everyone, "Here are the likelihood ratios, update your beliefs appropriately." Though that may lack some practicality.

noelwelsh 16y ago

Counterpoint: Last time I fitted a Bayesian model, I had 1 processor with some interpreted code and left it for ten minutes. YMMV.

bluekeybox 16y ago
Absolutely. Not all applications of Bayesian analysis are computationally-intensive. In some cases (an example: finding of single-nucleotide polymorphisms in next-gen sequencing data), Bayesian analysis comes down to multiplying prior probability of a SNP (for humans, 0.001 per genome position) by a few other numbers from the data itself to obtain posterior probability, which can be done in a linear time in a few minutes on tens of gigabytes of NGS data. And the best part is, no Bonferroni adjustment bullshit!
- hessenwolf 16y ago
  Hey, don't forget the Benjamini-Hochberg replacement for Bonferroni. The situation is not 'that' bad.

tel 16y ago

A point by point rebuttal. 1. The op knows this but is implicitly believing his audience is different from the cited fields often enough to make point (1) relevant. Moreover, it's perfectly correct to say that many journal reviewers are not interested in Bayesian methods. 2.1. This is highly anecdotal and not at all a strong point. I'm sure Efron has knocked out a 600 core cluster doing frequentist bootstrapping (1). I'm also certain that many, many Bayesian methods run near instantly on modern hardware. I'll concede that there are fewer closed form results, though. 2.2 This is very true. Entrepreneurs? 3. "Data so sparse, expert judgement so rich" is exactly where Bayesian analysis is most pertinent. Use a prior to clarify and quantify your expert opinion and then demonstrate that indeed your few observations are worthy to change someone's opinion. 4. Choice of frequentist testing regime introduces subjectivity, too! Moreover, since these have been heralded as "objective" for so long it's pretty difficult to get people to recognize as much. Oftentimes, a frequentist method will be equivalent to a Bayesian method under a maximally uninformed prior. This is still a subjective assumption (though there are benefits of such a prior). --- Frequentists test are oftentimes very necessary. They have already been highly optimized in many cases and thus are available on low resource computing platforms. They are definitely an important engineering solution! That said, Bayesian methods do a far better job being clear in their assumptions and simple in their logic. There is certainly room for better software (free or otherwise) to replace BUGS/JAGS/whatever for the largest use cases of statistics in many fields. Also, another point you make about Bayesian methods making life difficult during certification and publication is exactly right, and probably the largest (unspoken) reason why they're not going to be used in core scientific fields for a long while. But both of those reasons are distinctly practical and unscientific. Bayesian methods do a better job using your data. They do this by allowing expert knowledge to enter into statistics in a sensible fashion. Finally, they introduce an easily understood interpretation on the answers to your statistical questions. You might not personally want to use them today for practical reasons, but the author of this article is very much in the right to try to encourage more scientists in more fields to take a look. (1) Sorry, I'm actually not at all sure if this is the case. Bootstrapping is still more computationally efficient than MCMC, I think. I just used the example because I think it's ridiculous to make either point.

hessenwolf 16y ago