I would urge you to follow ChristianKI’s advice, since I suspect you probably know much less than you think you know about either Bayesian or frequentist statistics. Perhaps you could explain in your own words why exactly it is clear that the ML book you are reading is “manifestly inferior” to your preferred approach?
Perhaps you could explain in your own words why exactly it is clear that the ML book you are reading is “manifestly inferior” to your preferred approach?
There is a bit of confusion here. I’m not stating that frequentist machine learning is inferior to Bayesian machine learning. I’m stating that Bayesian probability is superior to frequentist probability. How do I say this? Because in all the case that I know, either a Bayesian model can be reduced to a frequentist one or a Bayesian model gives more accurate prediction.
That said, not even this is a problem. Since I’m learning the subject, I’m not at the stage of saying “this sentence is wrong”. I’m at the stage of “this sentence doesn’t make sense in the context of Bayesianism”. So I’m asking “is there a book that teaches ML from a Bayesian point of view?”. The answer I’m discovering, appallingly but maybe not so, is no.
As for the fervent defence, under the premises elucidated in the comments, I hold none of the myths, so it doesn’t apply.
Because in all the case that I know, either a Bayesian model can be reduced to a frequentist one or a Bayesian model gives more accurate prediction.
I typically see this stated as “there is a Bayesian interpretation for every effective statistical technique.” As pointed out elsewhere, typically people use “frequentist” to mean “non-Bayesian,” which is not particularly effective as a classification.
So I’m asking “is there a book that teaches ML from a Bayesian point of view?”.
The answer I’m discovering, appallingly but maybe not so, is no.
Did you google Bayesian Machine Learning, or search for it on Amazon? Barber is a well-rated textbook available online for free. (I haven’t read it; Sebastien Bratieres thinks it’s comparable to Murphy, the second most popular ML book, which is Bayesian.) Incidentally, Bishop, the most popular ML book, is also Bayesian. You managed to find the only ML textbook I’ve seen which has, as a comment in one of the Amazon reviews, a positive comment that the book is not Bayesian!
The more meta point here is to not let a worldview shut you out from potentially useful resources. Yes, Bayesianism is the best philosophy of probability, but that does not mean it is the most effective practice of statistics, and excluding concepts or practices from your knowledge of statistics because of a disagreement on philosophy is parochial and self-limiting.
As pointed out elsewhere, typically people use “frequentist” to mean “non-Bayesian,” which is not particularly effective as a classification.
Reducing a frequentist model to a Bayesian one though it’s not a pointless excercise, since it elucidates the hidden assumptions, and at least you are better aware of its field of applicability.
Did you google Bayesian Machine Learning, or search for it on Amazon?
Only after buying the book I have :/
Bishop though seems a lot interesting, thanks!
The more meta point here is to not let a worldview shut you out from potentially useful resources.
Thankfully, I’m learning ML for my own education, it’s not something I need to practice right now.
You’re welcome! I should point out that the other words I was considering using to describe Bishop are “classic” and “venerable”—it’s not out of date (most actively used ML methods are surprisingly old), but you may want to read it in parallel with Barber. (In general, if you’ve never read textbooks in parallel before, I recommend it as a lesson in textbook design / pedagogy.)
I would urge you to follow ChristianKI’s advice, since I suspect you probably know much less than you think you know about either Bayesian or frequentist statistics. Perhaps you could explain in your own words why exactly it is clear that the ML book you are reading is “manifestly inferior” to your preferred approach?
Also consider reading this: A Fervent Defense of Frequentist Statistics.
There is a bit of confusion here. I’m not stating that frequentist machine learning is inferior to Bayesian machine learning. I’m stating that Bayesian probability is superior to frequentist probability.
How do I say this? Because in all the case that I know, either a Bayesian model can be reduced to a frequentist one or a Bayesian model gives more accurate prediction.
That said, not even this is a problem. Since I’m learning the subject, I’m not at the stage of saying “this sentence is wrong”. I’m at the stage of “this sentence doesn’t make sense in the context of Bayesianism”. So I’m asking “is there a book that teaches ML from a Bayesian point of view?”.
The answer I’m discovering, appallingly but maybe not so, is no.
As for the fervent defence, under the premises elucidated in the comments, I hold none of the myths, so it doesn’t apply.
I typically see this stated as “there is a Bayesian interpretation for every effective statistical technique.” As pointed out elsewhere, typically people use “frequentist” to mean “non-Bayesian,” which is not particularly effective as a classification.
Did you google Bayesian Machine Learning, or search for it on Amazon? Barber is a well-rated textbook available online for free. (I haven’t read it; Sebastien Bratieres thinks it’s comparable to Murphy, the second most popular ML book, which is Bayesian.) Incidentally, Bishop, the most popular ML book, is also Bayesian. You managed to find the only ML textbook I’ve seen which has, as a comment in one of the Amazon reviews, a positive comment that the book is not Bayesian!
The more meta point here is to not let a worldview shut you out from potentially useful resources. Yes, Bayesianism is the best philosophy of probability, but that does not mean it is the most effective practice of statistics, and excluding concepts or practices from your knowledge of statistics because of a disagreement on philosophy is parochial and self-limiting.
Reducing a frequentist model to a Bayesian one though it’s not a pointless excercise, since it elucidates the hidden assumptions, and at least you are better aware of its field of applicability.
Only after buying the book I have :/ Bishop though seems a lot interesting, thanks!
Thankfully, I’m learning ML for my own education, it’s not something I need to practice right now.
You’re welcome! I should point out that the other words I was considering using to describe Bishop are “classic” and “venerable”—it’s not out of date (most actively used ML methods are surprisingly old), but you may want to read it in parallel with Barber. (In general, if you’ve never read textbooks in parallel before, I recommend it as a lesson in textbook design / pedagogy.)
Using Bishop in my class this Fall, very popular for good reason.