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Bayesian here. 'The theory that would not die' is a wonderful read, and notes how many scientists used Bayesian techniques (subjective probability) in a variety

by arcanus 10y ago

Bayesian here. 'The theory that would not die' is a wonderful read, and notes how many scientists used Bayesian techniques (subjective probability) in a variety of contexts while the field was still unpopular (arguably, heretical) in the mainstream statistical community. Bayes was nevertheless used to inform ballistics calculations, help crack the enigma code, or inform search patterns for lost nuclear weapons. I don't think we have seen the end of Bayes, either, as it is very useful for uncertainty quantification in the engineering Sciences, machine learning techniques, or even discovering the Higgs Boson.

krastanov 10y ago
To your last paragraph: most of my fellow physicists do not care much about the distinction between bayesian and frequentist approaches, they just use a prepackaged tool to process their data. But of the few that learn those tools intimately, cosmologists are always bayesians (because we have only one Universe), and experimental particle physicists are always frequentist (because generating a ton of repeated test events is "cheap").
- jessriedel 10y ago
  The particle physics do sometimes make Bayesian plots and end up arguing over priors. See this, for example: https://cds.cern.ch/record/1375842/files/ATL-PHYS-PUB-2011-011.pdf https://cds.cern.ch/record/1375842/files/ATL-PHYS-PUB-2011-0... And I've heard more than one confused argument between particle physicists about whether Bayesianism or frequentism is "right"...
  - asher_ 10y ago
    Given the high level of statistical certainty that particle physicists strive for from experimental data, how much of a difference does the approach matter by the point where they are confident enough to endorse the theory? For instance, if you are going for five sigma (like in the Higgs Boson discovery), how would the different techniques change when the data is considered sufficient, if at all?
    - jessriedel 10y ago
      The five-sigma threshold is a limit for making big public announcements, or claiming definitive discrete discoveries (like new particles) that non-particle physicists can take for granted. But there's lots of intermediate work done at accelerators that can't reach that threshold but still informs further work, and even influences the construction and design of the next multi-billion dollar machines. The bi-annual Review of Particle Physics is as thick as a phone book and filled with (usually) 95%-confidence intervals for measured values. For instance, here is the 89-page summary of what is known about the tau particle: http://pdg.lbl.gov/2015/listings/rpp2015-list-tau.pdf http://pdg.lbl.gov/2015/listings/rpp2015-list-tau.pdf
j_s 10y ago
The Theory That Would Not Die: How Bayes' Rule Cracked the Enigma Code, Hunted Down Russian Submarines, and Emerged Triumphant from Two Centuries of Controversy https://amzn.com/dp/B0050QB3EQ https://amzn.com/dp/B0050QB3EQ
amasad 10y ago
As someone new to Bayesianism I'd be interested in hearing your experience applying it in day-to-day life. How useful do you think it is to the ordinary Joe? From my brief experience, after learning Bayes, my intuition about things involving probabilities grew very different than the people around me. For example, my friends were planning a skydiving trip and I googled the name of the skydiving business and found that they had fatal incidents in the past. My friends were convinced that given that they had incidents either the probability that we would have an incident using them doesn't change. Or worse, that since it has already happened, it's now less likely that an incident would happen. Wat.
- Houshalter 10y ago
  I think your anecdote is good. Knowing the formula for Bayes theorem isn't necessary, and I've never needed to try to do the math in my head or anything. But understanding probability theory, and having an intuitive feel for what probability is, is really important.
- mikekchar 10y ago
  The one place I've used Bayes (hopefully properly!) is in a spaced repetition flash card program. Usually spaced repetition algorithms wait a certain amount of time based on how many times you have seen and remembered a card. The more times you have remembered it, the longer you wait. It then creates a schedule for each day. You review the cards that have "expired" their wait time. I wanted to turn this upside down. Instead I sorted the cards by how likely you would be to remember them (a function of the number of times you have already remembered it and the amount of time that has passed since you last saw it). I put the least likely to be remembered cards first and the most likely to be remembered cards last. As you reviewed the cards you would either remember them or not remember them. I supposed that cards grouped together had a similar probability of being remembered (only valid if my sort algorithm was correct, but I had some confidence on that since I based it on somebody else's research ;-) ). I then used Bayes to estimate the probability of getting the card correct. So instead of scheduling the cards, I simply had the user keep reviewing until I had a certain confidence that there was a 90% or more of getting the cards correct. At which point I left the rest for another day (the probability goes down over time). Interestingly as the test is binary (remembered/not remembered) Bayes could be simplified down to getting it right a certain number of times in a row. This was simpler for the user to understand without significantly reducing the accuracy of the estimate. Gratifyingly the system worked incredibly well. I added an option to continue drilling the cards after you hit the 90% mark and I very rarely got into a situation where the estimate was wrong.
hacker42 10y ago
As a Bayesian you can maybe answer my question here: https://news.ycombinator.com/item?id=11985312 https://news.ycombinator.com/item?id=11985312 I would appreciate it!

by arcanus 10y ago

krastanov 10y ago

To your last paragraph: most of my fellow physicists do not care much about the distinction between bayesian and frequentist approaches, they just use a prepackaged tool to process their data. But of the few that learn those tools intimately, cosmologists are always bayesians (because we have only one Universe), and experimental particle physicists are always frequentist (because generating a ton of repeated test events is "cheap").

jessriedel 10y ago
The particle physics do sometimes make Bayesian plots and end up arguing over priors. See this, for example: https://cds.cern.ch/record/1375842/files/ATL-PHYS-PUB-2011-011.pdf https://cds.cern.ch/record/1375842/files/ATL-PHYS-PUB-2011-0... And I've heard more than one confused argument between particle physicists about whether Bayesianism or frequentism is "right"...
- asher_ 10y ago
  Given the high level of statistical certainty that particle physicists strive for from experimental data, how much of a difference does the approach matter by the point where they are confident enough to endorse the theory? For instance, if you are going for five sigma (like in the Higgs Boson discovery), how would the different techniques change when the data is considered sufficient, if at all?
  - jessriedel 10y ago
    The five-sigma threshold is a limit for making big public announcements, or claiming definitive discrete discoveries (like new particles) that non-particle physicists can take for granted. But there's lots of intermediate work done at accelerators that can't reach that threshold but still informs further work, and even influences the construction and design of the next multi-billion dollar machines. The bi-annual Review of Particle Physics is as thick as a phone book and filled with (usually) 95%-confidence intervals for measured values. For instance, here is the 89-page summary of what is known about the tau particle: http://pdg.lbl.gov/2015/listings/rpp2015-list-tau.pdf http://pdg.lbl.gov/2015/listings/rpp2015-list-tau.pdf

j_s 10y ago

The Theory That Would Not Die: How Bayes' Rule Cracked the Enigma Code, Hunted Down Russian Submarines, and Emerged Triumphant from Two Centuries of Controversy https://amzn.com/dp/B0050QB3EQ https://amzn.com/dp/B0050QB3EQ

amasad 10y ago

As someone new to Bayesianism I'd be interested in hearing your experience applying it in day-to-day life. How useful do you think it is to the ordinary Joe? From my brief experience, after learning Bayes, my intuition about things involving probabilities grew very different than the people around me. For example, my friends were planning a skydiving trip and I googled the name of the skydiving business and found that they had fatal incidents in the past. My friends were convinced that given that they had incidents either the probability that we would have an incident using them doesn't change. Or worse, that since it has already happened, it's now less likely that an incident would happen. Wat.

Houshalter 10y ago
I think your anecdote is good. Knowing the formula for Bayes theorem isn't necessary, and I've never needed to try to do the math in my head or anything. But understanding probability theory, and having an intuitive feel for what probability is, is really important.
mikekchar 10y ago
The one place I've used Bayes (hopefully properly!) is in a spaced repetition flash card program. Usually spaced repetition algorithms wait a certain amount of time based on how many times you have seen and remembered a card. The more times you have remembered it, the longer you wait. It then creates a schedule for each day. You review the cards that have "expired" their wait time. I wanted to turn this upside down. Instead I sorted the cards by how likely you would be to remember them (a function of the number of times you have already remembered it and the amount of time that has passed since you last saw it). I put the least likely to be remembered cards first and the most likely to be remembered cards last. As you reviewed the cards you would either remember them or not remember them. I supposed that cards grouped together had a similar probability of being remembered (only valid if my sort algorithm was correct, but I had some confidence on that since I based it on somebody else's research ;-) ). I then used Bayes to estimate the probability of getting the card correct. So instead of scheduling the cards, I simply had the user keep reviewing until I had a certain confidence that there was a 90% or more of getting the cards correct. At which point I left the rest for another day (the probability goes down over time). Interestingly as the test is binary (remembered/not remembered) Bayes could be simplified down to getting it right a certain number of times in a row. This was simpler for the user to understand without significantly reducing the accuracy of the estimate. Gratifyingly the system worked incredibly well. I added an option to continue drilling the cards after you hit the 90% mark and I very rarely got into a situation where the estimate was wrong.

hacker42 10y ago

As a Bayesian you can maybe answer my question here: https://news.ycombinator.com/item?id=11985312 https://news.ycombinator.com/item?id=11985312 I would appreciate it!