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Wondered why we have '-1' in "2 * Phi(asin((R - r) / sigma) - 1" in the golf example model. (Min 51:38)

Anyone know why loo_predict(blm) and predict(blm[-obs.,],data[obs,]) are giving me a predicted odds of 0.28 and 0.8 respectively? These estimates are so far apart. “0.8” seems more accurate to me but the events true outcome was “0” so loo_predict did a better job. Does loo_predict just not work with high Pareto values, is that why??

I came across this after I was explaining square root and exponents equating to 2d and 3D structures. As I understand it a 4d anything would appear as a 3D object to us. However, though we cannot see it, are we able to interact? If so how would we measure those interactions. Further more, the interest in tools that seemingly fit this standard to measure that in which we can not perceive.

How to consider priors in Bayesian regression with some data

She’s awesome

Lmfao this guys so funny

Just beginning to learn about Bayesian analysis ... thanks for the great video and everyone for links in comments ... Question: Is it correct to say that, in the world cup example 1, the only variables that are calculated by Stan are: b real, sigma_a >=0, and sigma_y >=0? In other words, Stan figures out (simultaneously/jointly): (1) the best b and sigma_a for the equation a = b*prior_scores + sigma_a*[eta_a ~ N(0,1)] (2) the best sigma_y so that student_t(df = 7, a[team_1] - a[team_2], sigma_y) best predicts ~ sqrt_adjusted[score(team_1) - score(team_2)] That seems kind of weird to me that after we figure out the formula for a, it kind of boils down to just one parameter = sigma_y

Thanks for this! Most interesting and useful

Wow! Brilliant - this really helped me a lot. Thank you.

Contain information would been shared ,👍🏻

Is it possible to include a factor variable in the model? If yes any examples please.

Most excellent!!!

Great

What's with the blurred box in the bottom of the slides?

As a clinician treating patients CATE (conditional average treatment effect) is not adequate. What is needed is conditional treatment effect distribution (C-TED). We need to know what the risk of bad outcomes is, for each treatment option, including varying the timing, dose and protocol. We need to know if the tails of the C-TED can be anticipated, detected, and mitigated. For this reason we don't want to compute average outcomes, we need to propagate distributions through the model, from the observed variables, through the hidden (latent) variables to the outcomes. Knowing the shape of the distribution is critically important. In pathophysiology and therapeutics the causal effects may often be non-linear.

Very good explanation, Thanks!

That was beautiful - Thank you for the wonderful package, Paul!

Gelman is quite nice to listen to. His RL voice sounds different from his blog voice somehow

(1:35) "A lot of us...a lot of us are." The melancholy of that statement was so tangible :)

Thank you so much for building bmrs. You saved my master thesis, got me into Bayesian statistics and made me learn R, which is now a staple tool of my professional career.

What was the bug he fixed? I want to know how he solved the problem.

I know this is a rather old video but it is still highly relevant and useful. At 47:02 I don't think a standard EV calculation really does that situation justice. With high payout/low loss situations like that I think it is better to weight the payouts by their utility. For example losing $10 may have basically no subjective utility loss when compared to the subjective utility gained from having $100k. Lets say that to me having $100k has 20k times as much ultility as losing $10 does. When you switch from an ev calculation based on the win/loss to a subjective ultility of the payouts there is a drastic increase in the EV(although still negative in this case). e.g: win=10000 dollars lose = 10 dollars (win*5.4e-06)-((1-5.4e-06)*lose)= ~-$9.46 dollars win_util = 20000 utility points or "utils" lose_util = 1 utils (win_util*5.4e-06)-((1-5.4e-06)*lose_util)=-0.89 utils This is a simple example and we could for sure argue about the subjective utility values but I think overall it shows that the normal EV calculation doesn't really do the situation justice when you think about the utility of the win versus the utility of the loss. One could also flip this around and talk about the subjective utility of losing samples versus winning samples. Like say this was overall +ev but that the subjective value in winning so rarely was less than the subjective value loss from losing so often. I got this concept from game theory. There are plenty of examples, especially in poker, where doing something that is -ev right now could lead to a +ev situation later on. Poker players call that implied ev and an example could be calling with some sort of draw when the current raw pot odds don't justify it but you know that when you do make your hand that the profits gained will make up for the marginal loss now. So for example lets say I have some idea for a product or service that would earn $50k a year off a $100k investment. With using a fairly standard 10x income for valuation estimation I could say the subjective utility of winning that 100k is actually worth 50k utility points versus the 10k utility points implied by an even weighting. This specific situation would still be -ev though. All of that leads me down the path of seriously doubting most of rational economics.

At 16:27 you talk about checking predictive posterior distributions for games against their actual results to check if they are within their respective 95% CIs. Are these games training data or unseen data?

"Soccer games are random, it all depends how good the acting is"

Also really liked the golf putt example.

Thanks for this. Any link to the slides?

Prof. Gelman: At 19:00 you talk about checking how the model fits the data; are there any tools in Stan to avoid overfitting?

Thanks Dr. Gelman

That golf putting model is just about the coolest thing ever.

Thanks a lot for making this code available for download. That was really helpful for getting started in Stan.

Thanks for the great presentation and explanations on real models. This made me laugh: "working with live posterior"

The data set from (bit.ly/lc-loans) was modified for this video illustration. Could you provide the modified one that i can follow? Thanks