But the content in my post isn’t by Less Wrong, it’s by McGrayne.
The history in McGrayne’s book is an excellent substantiation of just how deep, serious, and long-standing the debate between frequentism and Bayesianism really is. If they want, they can check the notes at the back of McGrayne’s book and read the original articles from people like Fisher and Jeffreys. McGrayne’s book is full of direct quotes, filled with venom for the ‘opposing’ side.
But the content in my post isn’t by Less Wrong, it’s by McGrayne.
Fair point. Still, a person who hasn’t read the book can’t know whether lines such as “at age 62, Laplace — the world’s first Bayesian — converted to frequentism” are from the book or if they were something you came up when summarizing.
If they want, they can check the notes at the back of McGrayne’s book and read the original articles from people like Fisher and Jeffreys.
In previous discussions on the topic, I’ve seen people express the opinion that the fierce debates are somewhat of a thing of the past. I.e. yes there have been fights, but these days people are mostly over that.
In previous discussions on the topic, I’ve seen people express the opinion that the fierce debates are somewhat of a thing of the past. I.e. yes there have been fights, but these days people are mostly over that.
This is something I was told over and over again by professors, when I was applying to grad school for biostatistics and told them I was interested in doing specifically Bayesian statistics. They mistook my epistemological interest in Bayes as like… ideological alignment, I guess. This is how I learned 1. that there were fierce debates in the recent past and 2. most people in biology don’t like them or consider them productive.
They mistook my epistemological interest in Bayes as like… ideological alignment, I guess. This is how I learned 1. that there were fierce debates in the recent past and 2. most people in biology don’t like them or consider them productive.
I’m not sure that the debates were even THAT recent. I think your professsors are worried about a common failure mode that sometimes creeps up- people like to think they know the “one true way” to do statistics (or really any problem) and so they start turning every problem into a nail so that they can keep using their hammer, instead of using appropriate methodology to the problem at hand.
I see this a fair amount in data mining, where certain people ONLY use neural nets, and certain people ONLY use various GLMs and extensions and sometimes get overly-heated about it.
Thanks for the warning. I thought the only danger was ideological commitment. But—correct me if I’m wrong, or just overrecahing—it sounds like if I fail, it’ll be because I develop an expertise and become motivated to defend the value of my own skill.
if I fail, it’ll be because I develop an expertise and become motivated to defend the value of my own skill.
No, more like you’ll spend months (or more) pushing against a research problem to make it approachable via something in a Bayesian toolbox when there was a straightforward frequentist approach sitting there all along.
Because of its subject, your post in particular will obviously focus on those who care about the debate. It’s not about the practice of learning from data, it’s about the history of views on how to learn from data.
The criticism that it ignores those who utilize and do not theorize is wrong headed. The only thing that prevents it from being an outright bizarre accusation is that LW has repeatedly ignored the mere utilizers who are outside the academic debate when they should have been discussed and addressed.
But the content in my post isn’t by Less Wrong, it’s by McGrayne.
I strongly, strongly disagree. Even presenting unaltered material in a context not planned by the original author is a form of authorship. You have gone far, far beyond that by paraphrasing. You have presented an idea to a particular audience with media, you are an author, you are responsible.
If my friend asks to borrow a book to read, and I say “Which book” and he or she says “Whichever” I affect what is read and create the context in which it is read.
I literally just finished the book, and Luke’s paraphrase seems pretty apt. As presented by McGrayne, with specific quotes and punitive actions, the feud was brutal.
My problem, and likely the chatters’, is that by leading a team cheer for one audience, the larger neutral audience feels excluded. Doesn’t really matter whose words it was.
And while most of the history was very interesting, some of it felt cherry-picked or spun, adding to that feeling of team-ization.
I don’t think “neutral” is quite the right word for the audience in question. It may be the best one, but there is more to it, as it only captures the group’s view of itself, and not how others might see it.
The Bayesians (vegetarians) see the “neutrals” (omnivores) as non-understanding (animal-killers). The neutrals see themselves as partaking of the best tools (foods) there are, both Bayesian and frequentist (vegetable and animal), and think that when Bayesians call them “non-Bayesians” (animal-killers) the Bayesians are making a mistake of fact by thinking that they are frequentists (carnivores). Sometimes Bayesians even say “frequentist” when context makes it obvious they mean “non-Bayesian” (or that they are making a silly mistake, which is what the threatened “neutrals” are motivated to assume).
As neutrals is absolutely how those in the group in question see themselves, but also true is that Bayesians see them as heretics, (murderers of Bambi, Thumper, and Lambchop), or what have you, without them making a mistake of fact. The Bayesian theoretical criticisms should not be brushed aside on the grounds that they are out of touch with how things are done, and do not understand that it that most use all available tools (are omnivorous). They can be addressed by invoking the outside view against the inside view, or practice against theory, etc. (these are arguments in which Bayesians and frequentists are joined against neutrals) and subsequently (if the “neutrals” (omnivores) do not win against the Bayesians [and their frequentist allies {those favoring pure diets}] outright in that round) on the well worn Bayesian (vegetarian) v. frequentist (carnivore) battlegrounds.
This is quite possible, but there is some irony here—you have misrepresented the analogy by describing a three category grouping system by naming two of its categories, implying it is about opposites!
I think that people do this too often in general and that it is implicated in this debate’s confused character. Hence, the analogy with more than a dichotomy of oppositional groups!
Having said that, I find myself agreeing with kurokikaze; the vegetarian-omnivore-carnivore metaphor doesn’t help. The spilt blood (and spilt sap) distract from, and obscure, the “Three, not two” point.
But the content in my post isn’t by Less Wrong, it’s by McGrayne.
The history in McGrayne’s book is an excellent substantiation of just how deep, serious, and long-standing the debate between frequentism and Bayesianism really is. If they want, they can check the notes at the back of McGrayne’s book and read the original articles from people like Fisher and Jeffreys. McGrayne’s book is full of direct quotes, filled with venom for the ‘opposing’ side.
Fair point. Still, a person who hasn’t read the book can’t know whether lines such as “at age 62, Laplace — the world’s first Bayesian — converted to frequentism” are from the book or if they were something you came up when summarizing.
In previous discussions on the topic, I’ve seen people express the opinion that the fierce debates are somewhat of a thing of the past. I.e. yes there have been fights, but these days people are mostly over that.
I took this as a successful attempt at humor.
This is something I was told over and over again by professors, when I was applying to grad school for biostatistics and told them I was interested in doing specifically Bayesian statistics. They mistook my epistemological interest in Bayes as like… ideological alignment, I guess. This is how I learned 1. that there were fierce debates in the recent past and 2. most people in biology don’t like them or consider them productive.
I’m not sure that the debates were even THAT recent. I think your professsors are worried about a common failure mode that sometimes creeps up- people like to think they know the “one true way” to do statistics (or really any problem) and so they start turning every problem into a nail so that they can keep using their hammer, instead of using appropriate methodology to the problem at hand.
I see this a fair amount in data mining, where certain people ONLY use neural nets, and certain people ONLY use various GLMs and extensions and sometimes get overly-heated about it.
Thanks for the warning. I thought the only danger was ideological commitment. But—correct me if I’m wrong, or just overrecahing—it sounds like if I fail, it’ll be because I develop an expertise and become motivated to defend the value of my own skill.
No, more like you’ll spend months (or more) pushing against a research problem to make it approachable via something in a Bayesian toolbox when there was a straightforward frequentist approach sitting there all along.
Because of its subject, your post in particular will obviously focus on those who care about the debate. It’s not about the practice of learning from data, it’s about the history of views on how to learn from data.
The criticism that it ignores those who utilize and do not theorize is wrong headed. The only thing that prevents it from being an outright bizarre accusation is that LW has repeatedly ignored the mere utilizers who are outside the academic debate when they should have been discussed and addressed.
I strongly, strongly disagree. Even presenting unaltered material in a context not planned by the original author is a form of authorship. You have gone far, far beyond that by paraphrasing. You have presented an idea to a particular audience with media, you are an author, you are responsible.
If my friend asks to borrow a book to read, and I say “Which book” and he or she says “Whichever” I affect what is read and create the context in which it is read.
I literally just finished the book, and Luke’s paraphrase seems pretty apt. As presented by McGrayne, with specific quotes and punitive actions, the feud was brutal.
My problem, and likely the chatters’, is that by leading a team cheer for one audience, the larger neutral audience feels excluded. Doesn’t really matter whose words it was.
And while most of the history was very interesting, some of it felt cherry-picked or spun, adding to that feeling of team-ization.
I don’t think “neutral” is quite the right word for the audience in question. It may be the best one, but there is more to it, as it only captures the group’s view of itself, and not how others might see it.
The Bayesians (vegetarians) see the “neutrals” (omnivores) as non-understanding (animal-killers). The neutrals see themselves as partaking of the best tools (foods) there are, both Bayesian and frequentist (vegetable and animal), and think that when Bayesians call them “non-Bayesians” (animal-killers) the Bayesians are making a mistake of fact by thinking that they are frequentists (carnivores). Sometimes Bayesians even say “frequentist” when context makes it obvious they mean “non-Bayesian” (or that they are making a silly mistake, which is what the threatened “neutrals” are motivated to assume).
As neutrals is absolutely how those in the group in question see themselves, but also true is that Bayesians see them as heretics, (murderers of Bambi, Thumper, and Lambchop), or what have you, without them making a mistake of fact. The Bayesian theoretical criticisms should not be brushed aside on the grounds that they are out of touch with how things are done, and do not understand that it that most use all available tools (are omnivorous). They can be addressed by invoking the outside view against the inside view, or practice against theory, etc. (these are arguments in which Bayesians and frequentists are joined against neutrals) and subsequently (if the “neutrals” (omnivores) do not win against the Bayesians [and their frequentist allies {those favoring pure diets}] outright in that round) on the well worn Bayesian (vegetarian) v. frequentist (carnivore) battlegrounds.
I think vegetarian-carnivore metaphor here doesn’t help at all :)
I found it helpful. But I’m an omnivore so I (mistakenly) think that I don’t have a dog in that fight.
This is quite possible, but there is some irony here—you have misrepresented the analogy by describing a three category grouping system by naming two of its categories, implying it is about opposites!
I think that people do this too often in general and that it is implicated in this debate’s confused character. Hence, the analogy with more than a dichotomy of oppositional groups!
Realising that it is a three-way split, not a two-way split is my latest hammer. See me use it in Is Bayesian probability individual, situational, or transcendental: a break with the usual subjective/objective bun fight.
Having said that, I find myself agreeing with kurokikaze; the vegetarian-omnivore-carnivore metaphor doesn’t help. The spilt blood (and spilt sap) distract from, and obscure, the “Three, not two” point.