[HN Gopher] Statistical Rethinking (2022 Edition)
___________________________________________________________________
Statistical Rethinking (2022 Edition)
Author : eternalban
Score : 323 points
Date : 2022-01-16 14:50 UTC (8 hours ago)
(HTM) web link (github.com)
(TXT) w3m dump (github.com)
| xayfs wrote:
| elcapitan wrote:
| Does the course require prior statistical knowledge? Couldn't
| quite figure that out. It looks interesting, and there are python
| versions of the examples as well..
| agucova wrote:
| You'll probably need some basic notions of statistical
| distributions and data analysis; I recommend reading the first
| chapter of the book or the first lecture and seeing whether
| you're missing anything important.
| jonnycomputer wrote:
| I think you'll be fine, actually. I've read through the first
| edition, and it's kept pretty intuitive.
| qorrect wrote:
| I got through it without any priors :).
| elcapitan wrote:
| Thanks! :)
| reactspa wrote:
| There's so much great content on the internet that's not easily
| discoverable. This is one.
|
| If you have discovered a great resource for intuitively learning
| about fat-tailed-distributions related mathematics, please share.
| I have fallen into the Taleb rabbit-hole and would really like to
| gain an * _intuitive*_ understanding of what he 's talking about
| when he mentions topics such as gamma distributions, lognormal
| distributions, loglikelihood.
| laGrenouille wrote:
| > The unfortunate truth about data is that nothing much can be
| done with it
|
| This is a fairly strong statement that goes against a lot of
| other work in data science and information visualization (John
| Tukey, Edward Tufte, Jacques Bertin, Hadley Wickham, ...). For
| example, see [0] and [1].
|
| [0] https://en.wikipedia.org/wiki/Exploratory_data_analysis [1]
| https://courses.csail.mit.edu/18.337/2015/docs/50YearsDataSc...
| nightski wrote:
| You are leaving out a very important part of the sentence -
| "until we say what caused it". If you listen to the first few
| lectures you'll understand exactly what he intends with this
| sentence.
| laGrenouille wrote:
| Thanks, though I actually meant to copy the entire thing (my
| fault).
|
| My point was that a lot of people working in data analysis
| would (strongly) disagree with the idea that we need to model
| the data in order to do anything with it. Visualisations and
| tabulations can tell a lot without any mathematical
| formalism.
| chiefalchemist wrote:
| To your point...Data has context. It has a source. It likely
| has flaws and/or (so to speak) bias. To get anything of it
| It's essential to understand what went into it. Else you'll
| deceive yourself or your stakeholders and bad decisions will
| be made.
| hinkley wrote:
| This cleaves very close to an aphorism I stole mercilessly
| many years ago: charts are for asking questions, not
| answering them.
|
| "What caused it" is the answer, and a graph can reveal just
| as easily as it can conceal the cause. Lies, damn lies, and
| statistics.
| bigbillheck wrote:
| > The unfortunate truth about data is that nothing much can be
| done with it until we say what caused it
|
| Nonparametric methods say 'hi'.
| agucova wrote:
| This is taking the quote completely out of context, it's not
| the data itself that conveys useful information, it's the data
| combined with a causal model!
| akdor1154 wrote:
| I wanna buy the hardcopy textbook but still have access to an
| epub version - do any retailers allow this? Linked publisher site
| doesn't seem to.
| dang wrote:
| Past related threads:
|
| _Statistical Rethinking [video]_ -
| https://news.ycombinator.com/item?id=29780550 - Jan 2022 (10
| comments)
|
| _Statistical Rethinking: A Bayesian Course Using R and Stan_ -
| https://news.ycombinator.com/item?id=20102950 - June 2019 (14
| comments)
| canjobear wrote:
| I've been reading polemics and tutorials for at least 12 years
| now arguing for Bayesian methods over Frequentist methods. They
| all seem persuasive, and everyone seems to be convinced that
| Bayesianism is the future, and Frequentism was a mistake, and the
| change to a glorious future where Bayesian methods are the
| standard way to do stats is just round the corner. It hasn't
| happened.
|
| Meanwhile I've never read a real argument _for_ Frequentism and I
| don 't know where I'd find one, short of going back to Fisher who
| is not well known for clear writing.
|
| What is going on? Is the future of Bayesian statistics just more
| and more decades of books and articles and code notebooks and
| great presentations showing how great Bayesianism is? Is it just
| institutional inertia preventing Bayesian stats from becoming
| standard, or does Frequentism have a secret strength that keeps
| it hegemonic?
| agucova wrote:
| I guess it really depends on your discipline, but Bayesian
| methods have become more and more popular in a lot of academic
| communities, being published alongside papers using frequentist
| methods, so I wouldn't say it's hegemonic anymore.
| lkozma wrote:
| For those coming from a CS background a possible (crude)
| intuition sometimes given is that
|
| frequentist :: Bayesian ~ worst-case analysis :: average-case
| analysis
|
| There are a good reasons why we don't usually do average-case
| analysis of algorithms, chief among them that we have no idea
| how inputs are distributed (another reason is computational
| difficulty). Worst-case bounds are pessimistic, but they hold.
| civilized wrote:
| The biggest problem with Bayesian statistics in practice is the
| frequent reliance on relatively slow, unreliable methods such
| as MCMC.
|
| The Bayesian methodology community loves to advocate for
| packages like Stan, claiming that they make Bayesian stats
| easy. This is true... relative to Bayesian stats without Stan.
| But these packages are often much, much harder to get useful
| results from than the methods of classical statistics. You have
| to worry about all sorts of technical issues specific to these
| methodologies, because these issues don't have general
| technical solutions. Knowing when you even _have_ a solution is
| often a huge pain with sampling techniques like MCMC.
|
| So you have to become an expert in this zoo of Bayesian
| technicalities above and beyond whatever actual problem you are
| trying to solve. And in the end, the results usually carry no
| more insight or reliability than you would have gotten from a
| simpler method.
|
| I recommend learning about Bayesianism at a philosophical
| level. Every scientist should know how to evaluate their
| results from a Bayesian perspective, at least qualitatively.
| But don't get too into Bayesian methodology beyond simple
| methods like conjugate prior updating... unless you are lucky
| enough to have a problem that is amenable to a reliable,
| practical Bayesian solution.
| [deleted]
| 13415 wrote:
| I don't know much about statistical uses of Bayesianism but can
| say something opinionated about the underlying philosophy.
|
| From a philosophical point of view, Bayesianism is fairly weak
| and lacks argumentative support. The underlying idea of
| probabilism - that degrees of belief have to be represented by
| probability measures - is in my opinion wrong for many reasons.
| Basically the only well-developed arguments for this view are
| Dutch book arguments, which make a number of questionable
| assumptions. Besides, priors are also often not known. As far
| as I can see, subjective utilities can only be considered
| rational as long as they match objective probabilities, i.e.,
| if the agent responds in epistemically truth-conducive ways
| (using successful learning methods) to evidence and does not
| have strongly misleading and skewed priors.
|
| I also reject the use of simple probability representations in
| decision theory, first because they do not adequately represent
| uncertainty, second because they make too strong rationality
| assumptions in the multiattribute case, and third because there
| are good reasons why evaluations of outcomes and states of
| affairs ought to be based on lexicographic value comparisons,
| not just on a simple expected utility principle. Generally
| speaking, Bayesians in this area tend to choose too simple
| epistemic representations and too simple value representations.
| The worst kind of Bayesians in philosophy are those who present
| Bayesian updating as if it was the only right way to respond to
| evidence. This is wrong on many levels, most notably by
| misunderstanding how theory discovery can and should work.
|
| In contrast, frequentism is way more cautious and does not make
| weird normative-psychological claims about how our beliefs
| ought to be structured. It represents an overall more skeptical
| approach, especially when hypothesis testing is combined with
| causal models. A propensity analysis of probability may also
| sometimes make sense, but this depends on analytical models and
| these are not always available.
|
| There are good uses of Bayesian statistics that do not hinge on
| subjective probabilities and any of the above philosophical
| views about them, and for which the priors are well motivated.
| But the philosophical underpinnings are weak, and whenever I
| read an application of Bayesian statistics I first wonder
| whether the authors haven't just used this method to do some
| trickery that might be problematic at a closer look.
|
| I'd be happy if everyone would just use classical hypothesis
| testing in a pre-registered study with a p value below 1%.
| spekcular wrote:
| Regarding "and third because there are good reasons why
| evaluations of outcomes and states of affairs ought to be
| based on lexicographic value comparisons, not just on a
| simple expected utility principle": do you have any suggested
| references that describe this in more detail?
|
| Same question for "This is wrong on many levels, most notably
| by misunderstanding how theory discovery can and should
| work."
|
| Also, do you have any suggestions for statistics books that
| you _do_ like? Especially those with an applied bent (i.e.
| actually working with data, not philosophical discussions).
| kgwgk wrote:
| > The underlying idea of probabilism - that degrees of belief
| have to be represented by probability measures - is in my
| opinion wrong for many reasons. Basically the only well-
| developed arguments for this view are Dutch book arguments,
| which make a number of questionable assumptions.
|
| Why don't you consider Cox's theorem - and related arguments
| - well-developed?
|
| https://en.wikipedia.org/wiki/Cox%27s_theorem
| spekcular wrote:
| Dempster-Schafer theory is the obvious counterexample to
| "degrees of belief have to be represented by probability
| measures."
|
| https://en.wikipedia.org/wiki/Dempster%E2%80%93Shafer_theor
| y
| kgwgk wrote:
| Does is somehow imply that the Dutch book argument is
| better developed than Cox's argument?
| spekcular wrote:
| You asked, "Why don't you consider Cox's theorem - and
| related arguments - well-developed?" I consider Cox's
| argument not well-developed because D-S theory shows the
| postulates miss useful and important alternatives. So it
| fails as an argument for a particular interpretation of
| probability.
| kgwgk wrote:
| I quoted 13415 saying that the only well-developed
| arguments were [...] and asked him why didn't he consider
| [...] well-developed - compared to the former. I
| apologize if the scope of the question was not clear.
| 13415 wrote:
| That's an excellent question. The answer is that I don't
| really count such kind of theorems as positive arguments.
| They are more like indicators that carve out the space of
| possible representations of rational belief and basically
| amount to reverse-engineering when they are used as
| justifications. Savage does something similar in his
| seminal book, he stipulates some postulates for subjective
| plausibility that happen to amount to full probability (in
| a multicriteria decision-making setting). He motivates
| these postulates, including fairly technical ones, by
| finding intuitively compelling examples. But you can also
| find intuitively compelling counter-examples.
|
| To mention some alternative epistemic representations that
| could or have also been axiomatized: Dempster-Shafer
| theory, possibility theory by Dubois/Prade, Halpern's
| generalizations (plausibility theory), Haas-Spohn ranking
| theory, qualitative representations by authors like
| Bouyssou, Pirlot, Vincke, convex sets of probability
| measures, Josang's "subjective logic", etc. Some of them
| are based on probability measures, others are not. (You can
| find various formal connections between them, of course.)
|
| The problem is that presenting a set of axioms/postulates
| and claiming they are "rational" and others aren't is
| really just a stipulation. Moreover, in my opinion a good
| representation of epistemic states should at least account
| for uncertainty (as opposed to risk), because uncertainty
| is omnipresent. That can be done with probability measures,
| too, of course, but then the representation becomes more
| complicated. There is plenty of leeway for alternative
| accounts and a more nuanced discussion.
| kgwgk wrote:
| Thanks. I found interesting that you like the Dutch book
| arguments more than the axiomatic ones.
|
| > Moreover, in my opinion a good representation of
| epistemic states should at least account for uncertainty
| (as opposed to risk), because uncertainty is omnipresent.
|
| Maybe I'm misunderstading that remark because the whole
| point of Bayesian epistemology is to address uncertainty
| - including (but definitely not limited to) risk. See for
| example Lindley's book: Understanding Uncertainty.
|
| Now, we could argue that this theory doesn't help when
| the uncertainty is so deep that it cannot be modelled or
| measured in any meaningful way.
|
| But it's useful in many settings which are not about
| risk. One couple of examples from the first chapter of
| the aforementioned book: "the defendant is guilty", "the
| proportion of HIV [or Covid!] cases in the population
| currently exceeds 10%".
| 13415 wrote:
| Dutch book arguments are at least intended to provide a
| sufficient condition and are tied to interpretations of
| ideal human behavior, although they also make fairly
| strong assumptions about human rationality. The
| axiomatizations do not have accompanying uniqueness
| theorems. The situation is parallel in logic. Every good
| logic is axiomatized and has a proof theory, thus you
| cannot take the existence of a consistent axiom system as
| an argument for the claim that this is the one and only
| right logic (e.g. to resolve a dispute between an
| intuitionist and a classical logician).
|
| The point about uncertainty was really just concerning
| the philosophical thesis that graded rational belief _is_
| based on a probability measure. A simple probability
| measure is not good enough as a general epistemic
| representation because it cannot represent lack of belief
| - you always have P(-A)=1-P(A). But of course there are
| many ways of using probabilities to represent lack of
| knowledge, plausibility theory and Dempster-Shafer theory
| are both based on that, and so are interval
| representations or Josang 's account.
|
| I'll check out Lindley's book, it sounds interesting.
| kgwgk wrote:
| > "degrees of belief have to be represented by
| probability measures", "the philosophical thesis that
| graded rational belief is based on a probability measure"
|
| Of course it all depends on how we want to define things,
| we agree on that. There is some "justification" for
| Bayesian inference if we accept some constraints. And
| even if there are alternatives - or extensions - to
| Bayesian epistemology I don't think they have produced a
| better inference method (or any, really).
| agucova wrote:
| I'm following the course using Julia/Turing.jl and it's simply
| awesome.
|
| Richard McElreath clearly has a talent for teaching, and both the
| lectures and his book also give a very insightful discussion on
| the philosophy of science and common pitfalls of common
| statistical methods.
|
| Last semester I took my first classical Probability and
| Statistics course at my uni, and this course has been positively
| refreshing in comparison.
| rabaath wrote:
| This book is amazing. In my opinion the best book to get started
| with advanced statistics (all statistics, not just Bayesian
| statistics).
| huijzer wrote:
| One problem though. If you start with McElreath, you will
| likely find all books which require you to wrangle your brain
| into sided p-values and confidence intervals stupid
| ivan_ah wrote:
| Here is a direct link to the playlist:
| https://www.youtube.com/playlist?list=PLDcUM9US4XdMROZ57-OIR...
| Prof. McElreath has been adding two new videos every week.
|
| Also, for anyone who prefers to use the pythons for the coding, I
| recommend the PyMC3 notebooks https://github.com/pymc-
| devs/resources/tree/master/Rethinkin... There is also a
| discussion forum related to this repo here
| https://gitter.im/Statistical-Rethinking-with-Python-and-PyM...
| canyon289 wrote:
| Im one of the Core devs for Arviz and PyMC! Glad you found
| those resources useful. If any has any questions feel free to
| ask them in gitter and we'd be happy to help
| throwoutway wrote:
| Will there be other course schedules? These dates don't work for
| me unfortunately
| cinntaile wrote:
| Unless you're a student there you won't be able to attend the
| classes and get a grade anyway. You just watch the Youtube
| video's, he makes new ones each year.
| rossdavidh wrote:
| Also, having self-taught from the previous edition of his
| excellent book, I can say that it is very useful even if you
| aren't able to attend his class.
| canyon289 wrote:
| Self plug: After reading this book if you're looking to continue
| I recently published a book in the same series with the same
| publisher.
|
| The book is available for open access, though I appreciate folks
| buying a copy too! https://bayesiancomputationbook.com
|
| https://www.routledge.com/Bayesian-Modeling-and-Computation-...
| kfor wrote:
| Awesome book, Ravin! I'm waiting for my physical copy to arrive
| (should be here tomorrow!) before really diving in, but what
| I've skimmed in the digital copy so far is great.
|
| Btw I've been using PyMC2 since 2010 and contracted a bit with
| PyMC Labs, so I'm surprised we've never bumped into each other!
| sean_the_geek wrote:
| Thank you!
| spekcular wrote:
| Looks fun! Thanks for sharing. It seems like it covers
| complementary topics in a very concrete and clear way.
| canyon289 wrote:
| Thanks for checking it out and the feedback. I appreciate it!
| spekcular wrote:
| This is a great book.
|
| However, I really hate the "Golem of Prague" introduction. It
| presents an oversimplified caricature of modern frequentist
| methods, and is therefore rather misleading about the benefits of
| Bayesian modeling. Moreover, most practicing statisticians don't
| really view these points of view as incompatible. Compare to the
| treatment in Gelman et al.'s Bayesian Data Analysis. There are
| p-values all over the place.
|
| Most importantly, this critique fails on basic philosophical
| grounds. Suppose you give me a statistical problem, and I produce
| a Bayesian solution that, upon further examination with
| simulations, gives the wrong answer 90% of time on identical
| problems. If you think there's something wrong with that, then
| congratulations, you're a "frequentist," or at least believe
| there's some important insight about statistics that's not
| captured by doing everything in a rote Bayesian way. (And if you
| don't think there's something wrong with that, I'd love to hear
| why.)
|
| Also, this isn't a purely academic thought experiment. There are
| real examples of Bayesian estimators, for concrete and practical
| problems such as clustering, that give the wrong estimates for
| parameters with high probability (even as the sample size grows
| arbitrarily large).
| kkoncevicius wrote:
| > If you think there's something wrong with that, then
| congratulations, you're a "frequentist".
|
| And more than that - if you use bootstrap, or do cross-
| validation, you are being a frequentist.
| nightski wrote:
| Hmm. He was comparing the Bayesian models to golems as well,
| not just frequentist. It was an analogy to all statistical
| models.
|
| Second in the lectures he said that he uses frequentist
| techniques all the time and that it's often worth looking at it
| from each perspective.
|
| I interpreted it as his problem is not with the methods
| themselves, but with how they are commonly used in science. To
| me this made a lot of sense.
| spekcular wrote:
| I think I'm misremembering. I read through some of the
| introductory material in the second edition of his book and
| found it less critical than I recalled.
|
| But in some places, it definitely comes across as hostile
| (e.g. footnote 107).
|
| Also, the sentence "Bayesian probability is a very general
| approach to probability, and it includes as a special case
| another important approach, the frequentist approach" is
| pretty funny. I know the exact technical result he's
| referring to, but it's clearly wrong to gloss it like that.
|
| He does mention consistency once, page 221, but
| (unconvincingly) handwaves away concerns about it. (Large N
| regimes exist that aren't N=infinity...)
| nightski wrote:
| Honestly I think it is a little hostile. Not towards
| frequentist directly, but towards the mis-use of
| frequentist methods in science. He works in ecology and I
| think he comes across a bunch of crap all the time. He
| talks at length about the statistical crisis in science and
| I can't really blame him.
|
| But I could see how someone might take this as an attack on
| the methods themselves.
| kmonad wrote:
| I agree. The golem is presented as an analogue to any
| statistical inference: powerful but ultimately dumb, in
| the sense that it won't think for you. That's in my
| opinion the major theme of the book---you have to think
| and not rely on algorithms/tools/machines...or golems to
| do that for you.
| rwilson4 wrote:
| Gill's book, Bayesian Methods, is even more dismissive, and
| even hostile towards Frequentist methods. Whereas I've never
| seen a frequentist book dismissive of Bayes methods.
| (Counterexamples welcome!)
|
| It boils down to whether you give precedence to the likelihood
| principle or the strong repeated sampling principle (Bayes
| prefers the likelihood principle and Frequentist prefers
| repeated sampling). See Cox and Hinkley's Theoretical
| Statistics for a full discussion, but basically the likelihood
| principle states that all conclusions should be based
| exclusively on the likelihood function; in layman's terms, on
| the data themselves. This specifically omits what a frequentist
| would call important contextual metadata, like whether the
| sample size is random, why the sample size is what it is, etc.
|
| The strong repeated sampling principle states that the goodness
| of a statistical procedure should be evaluated based on
| performance under hypothetical repetitions. Bayesians often
| dismiss this as: "what are these hypothetical repetitions? Why
| should I care?"
|
| Well, it depends. If you're predicting the results of an
| election, it's a special 1 time event. It isn't obvious what a
| repetition would mean. If you're analyzing an A/B test it's
| easy to imagine running another test, some other team running
| the same test, etc. Frequentist statistics values consistency
| here, more so than Bayesian methods do.
|
| That's not to come out in support of one vs the other. You need
| to understand the strengths and drawbacks of each and decide
| situationally which to use. (Disclaimer: I consider myself a
| Frequentist but sometimes use Bayesian methods.)
| it_does_follow wrote:
| > Whereas I've never seen a frequentist book dismissive of
| Bayes methods
|
| Nearly every Frequentist book I have mentioning Bayesian
| method attempts to write them off pretty quickly as
| "subjective" (Wasserman, comes immediately to mind but there
| are others), which is falsely implying that some how
| Frequentist methods are some how more "objective" (ignoring
| the parts of your modeling that are subject does not somehow
| make you more object). The very phrase of the largely
| frequentist method "Empirical Bayes" is a great example of
| this. It's an ad hoc method that somehow implies that Bayes
| is not Empirical (Gelman et al specifically call this out).
|
| Until very recently Frequentist methods have near universally
| been the entrenched orthodoxy in most fields. Most Bayesians
| have spend a fair bit of their life having their methods
| rejected by people who don't really understand the foundation
| of their testing tools, but more so think their testing tools
| come from divine inspiration and ought not to be questioned.
| Bayesian statistics generally does not rely on any ad hoc
| testing mechanism, and can all be derived pretty easily from
| first principles. It's funny you mentioned A/B tests as a
| good frequentist example, when most marketers absolutely
| prefer their results interpreted as the "probability that A >
| B", which is the more Bayesian interpretation. Likewise the
| extension for A/B to multi-armed bandit trivially falls out
| of the Bayesian approach to the problem.
|
| Your "likelihood" principle discussion is also a bit
| confusing here for me. In my experience Fisherian schools
| tend to be the highest champions of likelihood methods.
| Bayesians wouldn't need tools like Stan and PyMC if they were
| exclusively about likelihood since all likelihood methods can
| be performed strictly with derivatives.
| periheli0n wrote:
| This sounds to me very much like a political debate between
| people arguing for the best method, rather than focusing on
| the results that you can get with either method.
|
| As long as this debate is still fuelled by emotional and
| political discourse, nothing useful will come out of it.
|
| What is really needed is an assessment which method is best
| suited for which cases.
|
| The practitioner wants to know "which approach should I
| use", not "which camp is the person I'm listening to in?"
| spekcular wrote:
| "Whereas I've never seen a frequentist book dismissive of
| Bayes methods. (Counterexamples welcome!)"
|
| Indeed! There's a lot of Bayesian propaganda floating around
| these days. While I enjoy it, I would also love to see some
| frequentist propaganda (ideally with substantive educational
| content...).
| kkoncevicius wrote:
| A book by Deborah Mayo "Statistical Inference as Severe
| Testing" might fit.
| spekcular wrote:
| I've read it. Unfortunately, I thought it was terribly
| written. Also, it's a philosophy book, not a guide for
| practitioners.
| kkoncevicius wrote:
| In my opinion books for practitioners is not the place
| for such discussions. Deborah's book might be poorly
| written, but if we want to go where the foundations of
| disagreements are we have to reach philosophy. Bayessian
| advocates are also often philosophers, like i.e. Jacob
| Feldman.
|
| From theoretical statisticians Larry Wasserman is more on
| the frequentist side. See for example his response on
| Deborah's blog [1]. But he doesn't advocate for it in his
| books. So yeah, besides Deborah, I am not aware of any
| other frequentist "propagandist".
|
| [1]
| https://errorstatistics.com/2013/12/27/deconstructing-
| larry-...
| huijzer wrote:
| > Indeed! There's a lot of Bayesian propaganda floating
| around these days. While I enjoy it, I would also love to
| see some frequentist propaganda
|
| I think that frequentist statistics doesn't need marketing.
| It's the default way to do statistics for everyone and,
| frankly, Bayesian software is still quite far away from
| frequentist software in terms of speed and ease of use.
| Speed will be fixed by Moore's law and better software and
| easy of use will also be fixed by better software at some
| point. McElreath and Gelman and many others do a great job
| in getting more people into Bayesian statistics which will
| likely result in better software in the long run
| siddboots wrote:
| All of Statistics by Larry Wasserman is a great
| introductory book from the frequentist tradition that
| includes some sections on Bayesian methods. It's definitely
| not frequentist propaganda - more like a sober look at the
| pros and cons of the Bayesian point of view.
| CrazyStat wrote:
| My first year of grad school I ordered a textbook but
| what I got was actually All of Statistics with the wrong
| cover bound on.
|
| I skimmed through a couple chapters before returning it
| for a refund. I sometimes regret not keeping it as a
| curio, but I was a poor grad student at the time and it
| was an expensive book.
| harry8 wrote:
| https://archive.org/details/springer_10.1007-978-0-387-21
| 736...
|
| Statistics & machine learning book authors seem to be
| really good at providing a free, electronic copy.
| qorrect wrote:
| > I consider myself a Frequentist
|
| Grab the pitchforks!
| valenterry wrote:
| Thank you! That's the kind of comments why I come here.
| 41b696ef1113 wrote:
| >Whereas I've never seen a frequentist book dismissive of
| Bayes methods.
|
| I think it more has to do with the long history of anti-
| Bayesianism championed by Fischer. He was a powerhouse who
| did a lot to undermine its use. The Theory that Would Not Die
| went into some of these details.
| [deleted]
| kgwgk wrote:
| Suppose you give me a particle physics problem, and I produce a
| quantum mechanics solution that, upon further examination, is
| wrong.
|
| If you think there's something wrong with that, then
| congratulations, you're a "quantum negationist," or at least
| believe there's some important insight about physics that's not
| captured by doing everything in a rote quantum way. (The
| important insight being that GIGO.)
| spekcular wrote:
| The issue isn't that Bayesian methods used incorrectly can
| have bad frequentist properties. It's that, according to many
| flavors of Bayesianism, having bad frequentist properties
| _isn 't a valid line of critique_.
|
| You may not believe in the particular stances I'm calling
| out, but if so, we don't disagree.
| kgwgk wrote:
| Maybe we don't disagree. You wrote:
|
| > a Bayesian solution that, upon further examination with
| simulations, gives the wrong answer 90% of time on
| identical problems
|
| If "with simulations" means either
|
| "with simulations using a probability distribution
| different from the prior used in the Bayesian analysis"
|
| or
|
| "with simulations using a model different from the one used
| in the Bayesian analysis"
|
| are we expected to conclude that there is something wrong
| with the Bayesian way?
| spekcular wrote:
| I mean "with simulations using a probability distribution
| [for the true parameter] different from the prior used in
| the Bayesian analysis." (The issue of model error is a
| separate question.)
|
| Yes, in this case would should conclude there is
| something wrong with the Bayesian way. If you hand me a
| statistical method to e.g. estimate some parameter that
| frequently returns answers that are far from the truth,
| that is a problem. One cannot assume the prior exactly
| describes reality (or there would be no point in doing
| inference, because the prior already gives you the
| truth).
| kgwgk wrote:
| At least a Bayesian posterior tries to describe reality.
| In a way which is consistent with the prior and the data.
| But again, GIGO.
|
| On the other hand, Frequentist methods do not claim
| anything concrete about reality. Only about long-run
| frequencies in hypothetical replications.
|
| You may think that makes them better, it's your choice.
| agucova wrote:
| I think the classes opt for starting with a simple mental model
| students can adopt, which is gradually replaced with a more
| robust and nuanced mental model.
|
| In this case he wasn't talking just about frequentist methods
| tho, it's also talking about doing statistics without first
| doing science (and formulating a causal model).
|
| I would be wary of jumping to conclusions from that
| introduction alone if you haven't seen the rest of the course
| or the book.
| funklute wrote:
| > There are real examples of Bayesian estimators, for concrete
| and practical problems such as clustering, that give the wrong
| estimates for parameters with high probability (even as the
| sample size grows arbitrarily large).
|
| Could you give some specific examples, and/or references? This
| is new to me, and I would like to read deeper into it.
| medstrom wrote:
| Uh, dude. If you read the book, you'd see the Golem of Prague
| isn't a parable about frequentist models specifically, it's
| about all models, period. He calls his Bayesian models golems
| all the time.
| jdreaver wrote:
| I've read this book and taken this course twice, and it is easily
| one of the best learning experiences I've ever had. Statistics is
| a fascinating subject and Richard helps bring it alive. I had
| studied lots of classical statistics texts, but didn't quite
| "get" Bayesian statistics until I took Richard's course.
|
| Even if you aren't a data scientist or a statistician (I'm an
| infrastructure/software engineer, but I've dabbled as the "data
| person" in different startups), learning basic statistics will
| open your eyes to how easy it is to misinterpret data. My
| favorite part of this course, besides helping me understand
| Bayesian statistics, is the few chapters on causal relationships.
| I use that knowledge quite often at work and in my day-to-day
| life when reading the news; instead of crying "correlation is not
| causation!", you are armed with a more nuanced understanding of
| confounding variables, post-treatment bias, collider bias, etc.
|
| Lastly, don't be turned off by the use of R in this book. R is
| the programming language of statistics, and is quite easy to
| learn if you are already a software engineer and know a scripting
| language. It really is a powerful domain specific language for
| statistics, if not for the language then for all of the
| statisticians that have contributed to it.
| agucova wrote:
| Even if you don't like R, your can do the entire course with
| Julia/Turing, Julia/Stan or Python, the course github's page
| has a list of "code translations" for all the examples.
| fault1 wrote:
| There is also other translations, for example, in
| pytorch/pyro: https://fehiepsi.github.io/rethinking-pyro/
|
| I would say statistical rethinking is a great way to compare
| and contrast different ppl impls and languages, I've been
| using it with Turing, which is pretty great.
| jonnycomputer wrote:
| I frequently prefer R to python/pandas/numpy for data analysis
| --even if most of my other programming is in python.
| elcapitan wrote:
| What's the advantage, if you already know Python? (genuine
| interest)
| Bootvis wrote:
| For me, I use R data.table a lot and I see as the main
| advantages are performance and the terse syntax. The terse
| syntax does come with a steep learning curve though.
| VeninVidiaVicii wrote:
| I totally agree. I often find myself wanting data.table
| as a standalone database platform or ORM-type interface
| for non-statistical programming too.
| boppo1 wrote:
| What is terse syntax? I can parse lisp and C, how would
| this be different and challenging?
| bckygldstn wrote:
| The syntax isn't self-describing and uses lots of
| abbreviations; it relies on some R magic that I found
| confusing when learning (unquoted column names and
| special builtin variables); and data.table is just a
| different approach to SQL and other dataframe libraries.
|
| Here's an example from the docs
| flights[carrier == "AA", lapply(.SD, mean),
| by = .(origin, dest, month), .SDcols =
| c("arr_delay", "dep_delay")]
|
| that's clearly less clear than SQL SELECT
| origin, dest, month, MEAN(arr_delay),
| MEAN(dep_delay) FROM flights WHERE carrier ==
| "AA" GROUP BY arr_delay, dep_delay
|
| or pandas flights[filghts.carrier ==
| 'AA'].groupby(['arr_delay', 'dep_delay']).mean()
|
| But once you get used to it data.table makes a lot of
| sense: every operation can be broken down to
| filtering/selecting, aggregating/transforming, and
| grouping/windowing. Taking the first two rows per group
| is a mess in SQL or pandas, but is super simple in
| data.table flights[, head(.SD, 2), by =
| month]
|
| That data.table has significantly better performance than
| any other dataframe library in any language is a nice
| bonus!
| kgwgk wrote:
| You mean something like SELECT
| origin, dest, month, AVG(arr_delay), AVG(dep_delay)
| FROM flights WHERE carrier == 'AA' GROUP
| BY origin, dest, month
|
| and flights[flights.carrier ==
| 'AA'].groupby(['origin', 'dest', 'month'])[['arr_delay',
| 'dep_delay']].mean()
| bckygldstn wrote:
| Yep thanks, you can tell I use a "guess and check"
| approach to writing sql and pandas...
| hervature wrote:
| Taking the first two rows is a mess in pandas?
|
| flights.groupby("month").head(2)
|
| Not only is does this have all the same keywords, but it
| is organized in a much clearer way to newcomers and
| labels things to look up in the API. Whereas your R code
| has a leading comma, .SD, and a mix of quotes and non-
| quotes for references to columns. You even admit the last
| was confusing to learn. This can all be crammed in your
| head, but not what I would call thoughtfully designed.
| [deleted]
| jarenmf wrote:
| Indeed, data.table is just awesome for productivity. When
| you're manipulating data for exploration you want the
| least number of keystrokes to bring an idea to life and
| data.table gives you that.
| jonnycomputer wrote:
| I don't want to say "advantage", so much as preference. But
| a few things come to mind.
|
| - Lots of high quality statistical libraries, for one
| thing.
|
| - RStudio's RMarkown is great; I prefer it to Jupyter
| Notebook.
|
| - I personally found the syntax more intuitive, easier to
| pick up. I don't usually find myself confused about the
| structure of the objects I'm looking at. For whatever
| reason, the "syntax" of pandas doesn't square well (in my
| opinion) with python generally. I certainly _want_ to just
| use python. But, shrug.
|
| - The tidyverse package, especially the pipe operator %>%,
| which afaik doesn't have an equivalent in Python. E.g.
| with_six_visits <- task_df %>%
| group_by(turker_id, visit) %>% summarise(n_trials
| = n_distinct(trial_num)) %>%
| mutate(completed_visit = n_trials>40) %>%
| filter(completed_visit) %>% summarise(n_visits =
| n_distinct(visit)) %>% mutate(six_visits =
| n_visits >= 6) %>% filter(six_visits) %>%
| ungroup()
|
| Here I'm filtering participants in an mturk study by those
| who have completed more than 40 trials at least six times
| across multiple sessions. It's not that I couldn't do the
| same transformation in pandas, but it feels very intuitive
| to me doing it this way.
|
| - ggplot2 for plotting; its really powerful data
| visualization package.
|
| Truthfully, I often do my data text parsing in Python, and
| then switch over to R for analysis, E.g. python's JSON
| parsing works really well.
| vcdimension wrote:
| R is used by many researchers and consequentially has many
| more statistical libraries (e.g. try doing a dynamic panel
| modelling in python).
| civilized wrote:
| Tabular data manipulation packages are better, easier to
| make nontrivial charts, many R stats packages have no
| counterparts in Python, less bureaucracy, more batteries-
| included.
|
| R is a language by and for statisticians. Python is a
| programming language that can do some statistics.
| swayson wrote:
| Julia, R (tidyverse), Python code examples available here:
|
| https://github.com/StatisticalRethinkingJulia
| https://github.com/pymc-devs/resources/tree/master/Rethinkin...
| https://bookdown.org/content/4857/
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