I'm trying to reproduce what you did in lecture 9. Getting stuck at the marginal/counterfactual example. Your (updated) code is this:

simulate as if all apps from women

p_G1 <- link(m2,data=list( D=rep(1:6,times=apps_per_dept), N=rep(1,total_apps), G=rep(1,total_apps)))

But m2 is the model from the simulated data, and if I understand correctly, here we are trying to mimic the real data. So I think it should be mGD. And indeed, in your script you have this:

OLD WRONG CODE!

#p_G1 <- link( mGD , data=list(N=dat$N,D=dat$D,G=rep(1,12)) )

I think mGD is the right model. Unfortunately when I use mGD with the updated code above, my result figure doesn't look like yours (it also doesn't look like yours when. Instead of the main peak at around 0.1, I get the main peak at 0 and a minor one at 0.2. I'm not sure what's going on and trying to figure things out. Any pointers appreciated. Thanks!

Hi, I think you said that the error from lecture 9 is fixed, but I'm wondering if an error is still there on slide 75 and 76?

For example on slide 75, there's this code where link refers to model m2:

# simulate as if all apps from women
p_G1 <- link(m2,data=list(
D=rep(1:6,times=apps_per_dept),
N=rep(1,total_apps),
G=rep(1,total_apps)))

But in the lecture code, the model now refers to mGD.

# simulate as if all apps from women
p_G1 <- link(mGD,data=list(
    D=rep(1:6,times=apps_per_dept),
    N=rep(1,total_apps),
    G=rep(1,total_apps)))

FYI many base R scatterplots are not rendering in the pdf slides.

Examples:

• Lecture 3 slides 25-28
• Lecture 3 slide 38
• Lecture 3 slide 40
• Lecture 4 slide 6
• Lecture 4 slide 8
• Lecture 5 slide 6
• Lecture 5 slides 13-4
• Lecture 6 slide 87

List above isn't all slides that have issues but a few for illustrative.

which book to buy?

Statistical Rethinking: A Bayesian Course with Examples in R and STAN Hardcover – March 16 2020 by Richard McElreath (Author) ? https://www.amazon.ca/Statistical-Rethinking-Bayesian-Course-Examples/dp/036713991X/ref=sr_1_2?gclid=Cj0KCQiA8vSOBhCkARIsAGdp6RRjxXH1pEPmwImkgZcRpJt3Dg4TtOtwemUP5kBetPmUsQKQW2WtA2IaAnkWEALw_wcB&hvadid=229990286577&hvdev=c&hvlocphy=9000920&hvnetw=g&hvqmt=e&hvrand=14759278627849499121&hvtargid=kwd-300572196203&hydadcr=3317_10311015&keywords=statistical+rethinking&qid=1641911963&sr=8-2

Hi Richard McElreath I am a recent undergrad with an interest in applied statistics. I am willing to learn more about statistics but the website says "This event has sold out online" Can you please let me in to attend your lectures?

I was unable to replicate the plot of the hypothetical effect of manipulating the perception of applicants' gender. Using the code provided in the repository, I got something like this: Instead of the one shown in the lecture:

Given the discussion of DAGs and categorical variables in the homework, would it make sense to add Chapter 5 to this week's reading on the README.md schedule?

Dear Prof. McElreath,

My colleagues and I are working through the exercises and we are wondering whether it's allowed to make our solutions publicly available in a, for example, blogdown website?

Regards, Mikhael

Hi Richard @rmcelreath ,

Thanks for this great course! I have been reading chapter two of the book and I can't see why for the marble example, successive multiplications will produce the same results as a single number of ways calculation. In the example we are calculating the number of ways to draw with replacement a blue marble followed by a white and then another blue marble (BWB), given there are four marbles in the bag, I can't seem to get the same result if I do calculation in three stages like:

Posterior = (Number of ways / Total number of ways) * Prior

Assuming Prior is 1 at the beginning.

| p | Blue | White | Blue | Product | |------| ------- | ------- | ------- | --------- | | 0.25 | 0.17 (1)| 0.50 (3)| 0.17 (1)| 0.0145 | | 0.50 | 0.33 (2)| 0.33 (2)| 0.33 (2)| 0.0360 | | 0.75 | 0.50 (3)| 0.17 (1)| 0.50 (3)| 0.0425 |

Calculate in one go:

| p | BWB | |------| ------- | | 0.25 | 0.15 (3)| | 0.50 | 0.40 (8)| | 0.75 | 0.45 (9)|

We can see the for example, P = 0.75, doing successive draw / multiply gives a posterior probability of 0.0425, but if we do this in one go in the second table, we get 9 out of 20 ways = 0.45 which doesn't match with 0.0425. Assuming the normalization (denominator) is always 1 for every stage?

For the proportion of water in globe example, I also tried to see if the probabilities are calculated individually, the product will be the same as a single calculation. If we had the sequence WWWLL:

| E | Likelihood | Posterior | How Posterior is calculated |------------| ---------- | ---------------------------- | --------------------------------------------------------------------------------- | W | p | 2p | (p * 1)/ integral from 0 to 1 {p*1} | WW | p | 3p^2 | (p * 2p)/ integral from 0 to 1 {p * 2p} | WWW | p | 4p^3 | (p * 3p^2)/ integral from 0 to 1 {p * 3p^2} | WWWL | 1-p | 20p^3 - 20p^4 | ((1-p) * 4p^3)/ integral from 0 to 1 {(1-p) * 4p^3} | WWWLL | 1-p | 60p^3 - 120p^4 + 60p^5 | ((1-p) * (20p^3−20p^4))/ integral from 0 to 1 {(1-p) * (20p^3−20p^4)}

And this is different if I use the binomial distribution formula directly, the result is:

$5!/(2!3!) * p^3 * (1-p)^2 = 10p^3 - 20p^4 + 10p^5

Same question in stack exchange: https://math.stackexchange.com/questions/4503794/bayesian-inference-multiplication

D'oh! APOLOGIES! I now see the tweet I saw was for 2022 not 2023. Missed my chance. I'm blaming COVID.

<< Ignore the following -- and feel free to delete this "issue" if someone knows how >>

Sorry if I simply missed a memo but I'm hoping to register for the course. Opening it to external participants was mentioned in a tweet a couple of weeks ago, but I've not seen any follow-up.

Does anyone know if this is a possibility? And, if so, is there a link to the registration process?

Thanks very much for any leads!

I am taking the course right now and I have just come across the Phoenix Wright meme on colliders. Thanks for bringing back good old memories! Loving the course so far!

Thank you for your great book, slides and YouTube lecture. I am struggling to read through your book (2nd edition). lppd CV equation, on page 218 of your book and on Lecture 7 slide, looks inconsistent with lppd equation on page 210 and lppd IS equation on page 218. I think "log" should be put before "1/S". Am I wrong?

In model m11.4, the model allows each monkey to have their own intercept but common treatment effect. I am not sure about the interpretation of the individual intercept when the treatment variable has index contrast. Does the intercept indicate the logit(p left) of an individual monkey when there is no treatment, and does this make sense when the smallest coding value of treatment is 1?

Sorry if the answer is obvious, but I haven't been able to wrap my head around this.

Thank you.

Hi professor @rmcelreath , thanks for the amazing book! Loving it!

I would like to know if it is possible to share some code around the residual plots of Chapter 5, more specific, Figure 5-4.

I am trying to replicate it, but finding some difficulties. I will keep trying anyway.

All the best, Edu

First of all thanks for the book and the video course. The motivation behind multilevel models is clear: partial pooling is an "adaptive compromise" between no pooling and complete pooling. In the video lecture-12 (https://speakerdeck.com/rmcelreath/statistical-rethinking-2022-lecture-12?slide=40) we show the "gain" of using partial pooling using "cross-validation". But the cross-validation score of partial pooling is very similar to the complete pooling.

1. For this particular example, are there any other (more convincing?) arguments (other than cross-validation) to use partial pooling against complete pooling?
2. Is it possible to create a "simple" example in which we observe a more "dramatic" U-shaped cross-validation line?

Thanks in advance.