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Interview on ”Bayesian Statistics the Fun Way”

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Re: Interview on ”Bayesian Statistics the Fun Way”

#31

Post by codesushi42 »

MR4D wrote: post #19
samch93 wrote: post #10

As someone who has a master's degree in statistics and often uses Bayesian statistics, I think we should not focus on whether one is a Bayesian or a frequentist, but rather be pragmatic and take the most practical approach to solving a statistical problem. Moreover, I think statistical education should start with frequentist concepts and then extend them to the Bayesian framework since the likelihood plays also a maj…

Respectfully, I find that people who are not statisticians overwhelming disagree with your point on which should be taught first. Bayesian is a natural order of inference for people. The whole concept of the black swan ("all swans are white") proves this out. Frequentist statistics is much less intuitive to people. My preference is for people to be able to use some statistics, and Bayesian gets them productive faster…

I would argue that the problem with frequentist statistics is that it aligns with humans' flawed intuition of how randomness works. People are inherently obsessed with finding patterns to support their hypotheses.

The problem is that what we perceive as random and extremely unlikely events are in fact much more probable than what we estimate from using Gaussian methods. And the frequentist approach helps to create this distortion by ignoring black swans.

Here's a great video demonstrating how people tend to misunderstand randomness: https://youtu.be/tP-Ipsat90c

Re: Interview on ”Bayesian Statistics the Fun Way”

#35

Post by moultano »

samch93 wrote: post #10

As someone who has a master's degree in statistics and often uses Bayesian statistics, I think we should not focus on whether one is a Bayesian or a frequentist, but rather be pragmatic and take the most practical approach to solving a statistical problem. Moreover, I think statistical education should start with frequentist concepts and then extend them to the Bayesian framework since the likelihood plays also a maj…

In an introductory course, we should be teaching people to collect enough data that any reasonable choice of prior or method doesn't matter that much.

Re: Interview on ”Bayesian Statistics the Fun Way”

#36

Post by analog31 »

moultano wrote: post #35
samch93 wrote: post #10

As someone who has a master's degree in statistics and often uses Bayesian statistics, I think we should not focus on whether one is a Bayesian or a frequentist, but rather be pragmatic and take the most practical approach to solving a statistical problem. Moreover, I think statistical education should start with frequentist concepts and then extend them to the Bayesian framework since the likelihood plays also a maj…

In an introductory course, we should be teaching people to collect enough data that any reasonable choice of prior or method doesn't matter that much.

I started college in 1982. At that time, calculators were common, but not computers. The data sets had to be small enough for us to work problems by hand. Not any more. I see no reason why a stats course can't start out with big bright data sets that are easy to analyze, then advance through more difficult problems where it becomes progressively easier to get things wrong, and thus requires more sophistication to think about problems.

I just want to add a bit more. It's quite easy today, to generate and play with random numbers. If you think you understand a process that has generated your data simulate it and run the simulated data through the same analysis. I do this for real -- I don't trust myself to choose the right statistical analysis, so I always test my chosen analysis with simulated data. If I can fool myself with simulated data, than my real data is probably fooling me too.

Re: Interview on ”Bayesian Statistics the Fun Way”

#37

Post by loup-vaillant »

moultano wrote: post #35
samch93 wrote: post #10

As someone who has a master's degree in statistics and often uses Bayesian statistics, I think we should not focus on whether one is a Bayesian or a frequentist, but rather be pragmatic and take the most practical approach to solving a statistical problem. Moreover, I think statistical education should start with frequentist concepts and then extend them to the Bayesian framework since the likelihood plays also a maj…

In an introductory course, we should be teaching people to collect enough data that any reasonable choice of prior or method doesn't matter that much.

That is often not possible.

Could we, for instance, collect enough data on typing discipline to end the static/dynamic typing once and for all? Enough data to overcome the priors of both static typing and dynamic typing proponents?

We could, but that would require pretty big sample sizes. Like 10,000 developers of various competence, working on 1,000 projects of various domains and difficulties for various amounts of time (from a few days to at least a few months). Who is ever going to fund that?

Until we get such a miracle controlled study, our respective priors will still matter.

Re: Interview on ”Bayesian Statistics the Fun Way”

#38

Post by loup-vaillant »

samch93 wrote: post #10

As someone who has a master's degree in statistics and often uses Bayesian statistics, I think we should not focus on whether one is a Bayesian or a frequentist, but rather be pragmatic and take the most practical approach to solving a statistical problem. Moreover, I think statistical education should start with frequentist concepts and then extend them to the Bayesian framework since the likelihood plays also a maj…

One approach gives the right answer. The other approach is more computationally tractable. Computers are pretty powerful now, so we can afford the correct answer much more often than we used to.

As for what is more natural… I've seen a (frequentist) introduction to statistics, and it simply did not make sense. Nothing was justified, you just had to learn the stuff by rote and apply it in situations that look like they could use one tool or another.

Probability theory on the other hand is pretty obvious. The axioms required to derive it are ridiculously few and ridiculously intuitive. From there you get the sum and product rules, and all the rest. Always made perfect sense to me.

Re: Interview on ”Bayesian Statistics the Fun Way”

#39

Post by loup-vaillant »

jonathanstrange wrote: post #22
hotdog999 wrote: post #9

Earlier quoted context omitted.

> like I've suddenly gone soft and introduced a bunch of nebulous, touchy-feely context into the objective truth This drives me nuts. If you haven't, check out the paper "Beyond subjective and objective in statistics" by Gelman and Hennig (2017). Right at the beginning they make the point that any analysis includes external information in many ways, such as adjusting variables for imbalance, how we deal with outliers…

This critique might come from the idea that having a good analytic model, or at least some valuable analytic insights, involves much more than assigning some priors. Of course, the two things don't exclude each other, but for some frequentists Bayesians have the wrong perspective - or at least that's the critique, whether it's true or not. Another issue that I personally have with Bayesianism is that I believe that a…

> assigning probabilities to singular events is only meaningful and admissible at all if there is a good analytic explanation for the respective propensity.

Wait a minute, you are making a type error here: probabilities are not propensities. They're degrees of belief. (And even if you disagree in general, this is a Bayesian context you're talking about.)

If I put a die on a table and hide it with a cup, you could still estimate your probability distribution about which face is up. My probability distribution would obviously be very different, since I put the die in there myself. (Replace "probability" by "betting ratio" or "degrees of belief" if it makes more sense to you.)

> The [probabilism] view does not have very strong foundations.

Read the first 2 chapters of Probability Theory: the Logic of Science, by E. T. Jaynes: "Plausible reasoning" and "The quantitative rules". It's very accessible, and you shall see how strong the foundations really are.

http://www.med.mcgill.ca/epidemiology/hanley/bios601/Gaussia...

Re: Interview on ”Bayesian Statistics the Fun Way”

#40

Post by loup-vaillant »

j7ake wrote: post #11
gdy wrote: post #8

Can anyone recommend a 'Bayesian statistics the hard way' book?

Probability Theory: The Logic of Science by Edwin Jaynes

http://www.med.mcgill.ca/epidemiology/hanley/bios601/Gaussia...

But really, the first two chapters aren't that hard.

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