[HN Gopher] The Bayesian Cringe (2021)
___________________________________________________________________
The Bayesian Cringe (2021)
Author : EndXA
Score : 34 points
Date : 2024-03-26 19:49 UTC (3 hours ago)
(HTM) web link (statmodeling.stat.columbia.edu)
(TXT) w3m dump (statmodeling.stat.columbia.edu)
| parpfish wrote:
| I noticed a shift in my attitude in strong priors when I switched
| from academia to industry and have only recently realized why.
|
| When doing an analysis in an academic setting, the goal is to get
| a paper past reviewers to be published. And the reviewers were
| adversaries that were trying to disprove your work (at best these
| were helpful critique; at worst they were bad-faith nit-pickers
| that were looking for any excuse to reject). If you did a
| Bayesian analysis in this setting, the mean reviewers would just
| just point to the priors and say "you can't justify that choice,
| REJECT".
|
| But in industry, there are no reviewers serving as adversarial
| gatekeepers. You may present analyses to a skeptical audience,
| but if they disagreed with your model priors you would work
| _with_ them to come up with a mutually agreeable model because
| you 're all on the same team.
| BugsJustFindMe wrote:
| > _But in industry, there are no reviewers serving as
| adversarial gatekeepers. You may present analyses to a
| skeptical audience, but if they disagreed with your model
| priors you would work with them to come up with a mutually
| agreeable model because you 're all on the same team._
|
| This experience may not be representative. The web is
| absolutely filled with anecdotes of gatekeeping and
| obstructionism within engineering orgs. The phrase "internal
| politics" comes immediately to mind.
| whatshisface wrote:
| Internal politics have nothing to do with the content, so I
| think their point stands. There's no reason not to do a
| Bayesian analysis, if your proposal fails it will certainly
| be for another reason.
| vundercind wrote:
| Most folks' experience of primary and secondary education, and
| maybe also undergrad, is similar, with the instructor as the
| adversary. How much more collaborative and _forgiving_ the
| "scary" "real world" is, was a real surprise after so many
| years of school.
| whatshisface wrote:
| It can easily go too far though, especial when somebody
| doesn't want to accept criticism. Then you're not a team
| player if you check the arithmetic.
| williamdclt wrote:
| It's definitely a learned soft skill to direct interactions to
| be collaborative rather than adversarial.
|
| It's too easy to fall into an adversarial discussion because of
| differing opinions (eg about code architecture) when really
| you're on the same team. I try to keep in mind (and convey) the
| image of "you and me side to side against the problem on the
| whiteboard" rather than "you and me against each other"
| sk11001 wrote:
| I still have no clue what Gelman is saying about anything ever,
| and this post is no exeception. He seems like a great guy in
| interviews and presentations but anything he writes or talks
| about is highly non-specific.
| bruturis wrote:
| Prior are now not subjective but useful, the OP is about the
| problem of choosing the best priors. The best options are
| informative priors (1) and regularizers (2). So, for example,
| choosing as prior a Laplace distribution for the unknown
| parameters is equivalent to the LASSO that is a well known way of
| obtaining sparse models with few coefficients. In (2) there is an
| example in which a prior suggest a useful regularization method
| for regression. In (3) the author discusses prior modeling.
|
| (1)
| https://en.wikipedia.org/wiki/Prior_probability#Informative_...
|
| (2) https://skeptric.com/prior-regularise/index.html
|
| (3)
| https://betanalpha.github.io/assets/case_studies/prior_model...
| kqr wrote:
| There's also the fact that a prior is really hard to explain to
| someone else. By definition, it's the unexplainable starting
| point!
|
| Yet when I lay out fairly tight Bayesian reasoning, there's
| always that one person sucking life out of the entire
| conversation with "Wait can you go back to that first number? How
| did you arrive at that?" and it's an unanswerable question
| because any attempt would have to start from another, more
| fundamental prior!
|
| Sometimes this person is reasonable and I can go, "Ah, we can try
| a different starting point. What's your prior?" but often enough
| the person gets stuck on the idea of subjective probability and
| everything derails.
|
| When it comes to important decisions, I've started hiding the
| prior with smoke and mirrors to redirect attention away from it.
| oldgradstudent wrote:
| What's the difference between a prior and a bias?
|
| How does one distinguish between the two?
| layer8 wrote:
| A prior is whatever you start with. There's literally no
| requirements. Bayes tells you how to update your priors,
| whatever they are, in the face of new data. Nothing more,
| nothing less. In principle it doesn't matter what priors you
| start with (how biased they are), in the sense that given
| enough data, your likelihoods will converge to what is really
| the case.
___________________________________________________________________
(page generated 2024-03-26 23:01 UTC)