Statistical Rethinking (2022 Edition)
github.com
github.com
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.
Here's an example from the docs
flights[carrier == "AA",
lapply(.SD, mean),
by = .(origin, dest, month),
.SDcols = c("arr_delay", "dep_delay")]
SELECT
origin, dest, month,
MEAN(arr_delay), MEAN(dep_delay)
FROM flights
WHERE carrier == "AA"
GROUP BY arr_delay, dep_delay
flights[filghts.carrier == 'AA'].groupby(['arr_delay', 'dep_delay']).mean()
flights[, head(.SD, 2), by = month]
SELECT
origin, dest, month, AVG(arr_delay), AVG(dep_delay)
FROM flights
WHERE carrier == 'AA'
GROUP BY origin, dest, month
flights[flights.carrier == 'AA'].groupby(['origin', 'dest', 'month'])[['arr_delay', 'dep_delay']].mean()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.
| Date | EventType |
events[
year(Date) == 2020L,
.(first_date = first(Date), last_date = last(Date), count = .N),
EventType
]
When exploring data, I wouldn't need or use any whitespace. How would your Pandas approach look like?
mask = events["Date"].year == 2020 events[mask].groupby("EventType").agg(first_date=("Date", min), last_date=("Date", max), count=("Date", len))
Anyway, I don't understand why terseness is even desirable. We're doing DS and ML, no project never comes down to keystrokes but ability to search the docs and debug does matter.
- How many events by type
- When did they happen
- Are there any breaks in the count, why?
- Some statistics on these events like average, min, max
and so on. Terseness helps me in doing this fast.
R is a language by and for statisticians. Python is a programming language that can do some statistics.
- 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()
- 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.
(The below is off topic, but I don't use R so I'd love to know whether I'm reading the code correctly)
"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."
A user with this pattern of trials seems like they would fit the above definition:
Session 1: 82 trials Session 2: 82 trials Session 3: 82 trials
But the code seems to want 6 distinct sessions with >40 trials each. Have I misunderstood?
Also, is 'mutate' necessary before 'filter' or is that just to make the intent of the code clearer to your future self?
There were 50 trials in each session; so I counted a session completed if they did more than 40 in that session. They needed to have completed at least six sessions.
The mutate is unnecessary. I forget why I did that.
%>% is now simply >|
Hence my question:
What's the advantage of Python if you already know R?
I've heard they have similarities. Is there anything Python does better than R in terms of statistical analysis, charting, etc.?
AFAIK in statistical modelling Python is better only in neural networks, so if you do not need to do fancy things with images, text, etc. you do not need Python. R is still the king.
In terms of charting and dashboards, I would say that if you work high level R and Python are both pleasant. R has ggplot, but Python has Plotly Express. R has Shiny, but Python has Dash and Streamlit. You can do great with both.
In Python, there is statsmodels. Here, you'll find a lot of GLM stuff, which is sort of an older approach. Modern inferential statistics, if not just Bayesian, is usually in the flavor of semi-parametric models that rely on asymptotics.
As R is used by professional researchers, it is simply more on the edge of things. Python has most of the "statistics course" schoolbook methods, but not much beyond that.
For example, it has become very common to have dynamic panel data which require dynamic models. Now if you want to do a Blundell-Bond type model in PYthon you have to... code it yourself using GMM, if it exists even.
For statistics, that's pretty much like saying you have a Deep Learning package that maybe has GRU but no transformer modules at all. So yeah, you can code it yourself. Or you use the other one.
https://github.com/StatisticalRethinkingJulia https://github.com/pymc-devs/resources/tree/master/Rethinkin... https://bookdown.org/content/4857/
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.
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-...
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!
Which company are you with? Perhaps we did bump into each other, asking to see if that is the case
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...
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?
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.
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.
I'm a scientist. How do I do this? I took one stats course, more than 30 years ago, and it was mostly proofs. Of course we learned Bayes' Theorem, and solved some problems using it.
What I don't remember is being taught to choose an "ism" for doing math, so it could easily be something that I'm already doing and taking for granted.
[1] https://journals.plos.org/plosmedicine/article?id=10.1371/jo...
[2] http://www.stat.columbia.edu/~gelman/research/unpublished/po...
There's the famous quip: "If an experiment needs statistics, then one ought to have done a better experiment." While this is a bit extreme, it does represent a tendency to design experiments, when possible, that lend themselves to relatively basic data analysis. In fact, I haven't ever calculated a p value in my work.
From what I've read about Bayesian statistics, I'm not sure it's going to get the life sciences out of the mess they're in. That's just my personal hunch. The trouble is, for justifiable reasons, life science research is hard, and is conducted with urgency due to the immediate need for solutions (such as vaccines, therapies for mental illness, or cynically, advertising revenue).
A good rule of thumb seems to be: Multiply p by 10. In other words, p < 0.05 is a coin toss. This rule seems to replicate the entire replication crisis. ;-)
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%.
Why don't you consider Cox's theorem - and related arguments - well-developed?
https://en.wikipedia.org/wiki/Dempster%E2%80%93Shafer_theory
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, Jøsang'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.
> 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%".
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.
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). [I know your comment was about the philosophical foundations, not about the statistical methods. But the alternative statistical methods do not have better philosophical foundations.]
> I know your comment was about the philosophical foundations, not about the statistical methods.
Absolutely, at the risk of sounding picky I have to say that you've answered to a comment I've never made.
> But the alternative statistical methods do not have better philosophical foundations.
Frequentism and the propensity view have better philosophical justifications, though. You may disagree, but that was the whole point of my first comment. We know that there are genuine stochastic processes with corresponding objective probabilities, for example. Frequentism also prevents incorrect applications of probability such as using statistics to predict the outcome of singular events based on mere conjecture about the priors. You can only do that with an analytic model.
Not really if the knowledge we care about is related to a concrete situation (rather than the frequency of something under hypothethical replications defined in some ad-hoc way). As you said, whether that's okay or not depends on the task.
If we care about whether there was life on Mars or whether Aduhlem is an effective treatment for Alzheimer's I don't think that frequentist inference has good philosophical support. Frequentist epistemology is not directly applicable.
Of course if you consider the frequentist methods themselves as genuine stochastic processes with corresponding objective probabilities (which also requires a valid model, by the way) you have good philosophical support to say things about those methods and their long-term frequency properties.
But this knowledge about the statistical methods used doesn't translate into knowledge about the existence of life on Mars or the efficacy of Aduhlem unless you are ready to make additional assumptions - 'philosophically unjustified' as they may be.
What I suggest is that Bayesian methods provide a framework to reason about the plausability of some statement about the world in a systematic way (unlike Frequentist methods, whatever the limitations in Bayesian methods).
> Sometimes you really need to gather more information and/or develop an analytic model.
Bayesian methods are definitely not a way to escape the need for an analytic model (including all the prior knowledge) and data gathering. What they provide is a mechanism to integrate the data using the model and calculate the impact of incremental information on our knowledge / uncertainty.
I’m not saying that it’s easy to have a good model and useful data for complex questions. But with Frequentist methods in addition to the model and the data you’d be missing the mechanism to use them in a meaningful way.
I wonder why do you say that Bayesians try to avoid randomized controlled trials for new drugs, by the way. Bayesian methods are increasingly used in randomized clinical trials.
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).
Note that it is possible to reformulate the Covid example into a Null hypothesis test at the cost of being less informative (e.g. Is the R0 significantly above 1?) but then the knowledge becomes less useful for making certain important decisions.
Anyways, my general impression is that Bayesian statistics are probably more useful for making good decisions that require precise numerical knowledge of certain types of information but maybe less useful for many of the sorts of conceptual issues philosophers are often interested in.
The anti-bayesian frequentist argument, especially re. priors has always reminded me of that story about Minksy and his student 'randomly wiring' his machine. http://magic-cookie.co.uk/jargon/mit_jargon.htm
" In the days when Sussman was a novice, Minsky once came to him as he sat hacking at the PDP-6.
"What are you doing?", asked Minsky.
"I am training a randomly wired neural net to play Tic-Tac-Toe" Sussman replied.
"Why is the net wired randomly?", asked Minsky.
"I do not want it to have any preconceptions of how to play", Sussman said.
Minsky then shut his eyes.
"Why do you close your eyes?", Sussman asked his teacher.
"So that the room will be empty."
At that moment, Sussman was enlightened. "
Until about a decade ago, it could be really hard to get papers accepted in my scientific field (geosciences, particularly geology) that used Bayesian methods because senior faculty and journal editors deemed them subjective and less rigorous than frequentist ‘letter tests’. This didn’t really change until more powerful computers and mathy scripting languages allowed the younger scientists to generate undeniable better results, particularly for very poorly conditioned inverse and parameter estimation problems. The wave of pro-Bayesian propaganda has been promulgated in no small part by the younger scientists who have been fighting for acceptance for a long time, but the resistance fought through editorial and grant proposal rejection letters, not blog posts, more powerful but less noisy. It’s also still unlikely to find Bayes stats classes in most geoscience departments, so practitioners are largely self-taught outside of Michigan, Caltech etc.
I imagine it’s been similar in many branches of science.
Check out the works of Deborah Mayo and her peers. She has several books and papers on the virtues of Frequentism that range from non technical to technical. Her blog is excellent as well.
errorstatistics.com
Also, check out modern formulations of frequentist models in terms of estimating equations, which add a certain level of coherence to frequentist models that approaches a Bayesian coherence.
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).
And more than that - if you use bootstrap, or do cross-validation, you are being a frequentist.
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.
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...)
But I could see how someone might take this as an attack on the methods themselves.
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.)
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...).
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-...
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
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.
Statistics & machine learning book authors seem to be really good at providing a free, electronic copy.
Grab the pitchforks!
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.
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?”
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.
I'm skeptical of this because Frequentist (likelihood) methods are a special case of Bayesian methods, with flat/uniform priors for parameters (and the "flatness" of a parameter is dependent on your chosen parameterization anyway; it's not a fixed fact about the model). So it's reasonably easy to figure out when frequentist methods will be effective enough (based on Bayesian principles), and when they won't.
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.)
You may not believe in the particular stances I'm calling out, but if so, we don't disagree.
> 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?
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).
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.
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.
Could you give some specific examples, and/or references? This is new to me, and I would like to read deeper into it.
Some other references:
https://www.jstor.org/stable/2241268
https://arxiv.org/abs/1308.6306
https://projecteuclid.org/journals/annals-of-mathematical-st...
In the vogue days of reversible jump MCMC I played with mixture estimation of the number of components under a basic prior (an approach which gives decent results in Figs 1 and 3), but I never used a Dirichlet process prior for this problem. This paper points out that even this simple approach is problematic because it’s only consistent if the true distribution is such a mixture, and in my case it definitely was not.
Anyway, one takeaway, esp. from sec 1.2.1, is that the Dirichlet process prior is not suitable for estimating #components in most cases; it favors small clusters. And indeed, the concept of estimating #components is tricky to begin with, as noted above.
Just because you can compute the posterior, doesn’t mean it’s saying what you think it is about the underlying true distribution!
Frequentists consider the model parameters fixed and allow the data to vary. Bayesian consider the data fixed and allow parameters to vary.
For example many software engineering, drug testing, and large scale pipelines want frequentist guarantees, because your system will have varying input data and you want theoretical bounds on what inferences you can make.
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.
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.
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)
I don't know much about Bayesian data analysis, but I think the first idea to be introduced is not the concept of likelihood, Bayes theorem is the foundation and I think it should be introduced before likelihood.
On slide 31, it is not clear if you state a causal model before making observations, you should indicate a prior before making the observation so that you can then see how it updates, if done otherwise you are using the observations to elaborate the model and then the analysis is crookely designed.
On slide 35, the concept of relative plausibility seems to me a little sloopy to develop some theory.
On slide 48: In the context of confidence intervals: "95% is obvious superstition". I think 95% interval is using a standard way to communicate results that gives a useful information and avoid the question of why you computed, for example, the 93.728 confidence interval.
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...
“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.
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.
Nonparametric methods say 'hi'.