Introduction to Bayesian data analysis - Part 2: Why use Bayes? 

This was so clearly and amusingly demonstrated. Great video!

I would be happy to delve further into interpretations of ML algorithms from Bayesian perspective which you talk about at around 20 minutes into the video. I get the linear regression, but curious to learn more.

"I think he is smoking tobacco, but i don't know" hahahaha

Great explanations, thanks! I just didn't quite understand why in the A/B testing you should accept only results that match "both" datasets at the same time. Is it simply to apply arithmetics to the resulting generated data (like you did for getting the difference between A and B)? Thanks!

finally i have an idea on how to apply Bayesian Analysis to optimization

Please, could you provide a method how to calculate alfa & beta parameters for a beta-distribution given a particular probability? I noticed that in your example, probability of success ranges between 5% and 15%, and you genarated a distribution with params Beta(3,28). So the question is how did you achieve those alfa = 3 and beta = 28, respectively? Thanks!

Dude, you're a hero!

Congrats for the videos. They are amazing. I really like the way you explain. It's brilliant. Also, your voice is a good fit for videos. When you calculate the profit for method B, shouldn't you take out 300 for the salmon and 30 still for the mail since you send both?

Why do we need to discard samples when only one draw matches the real data (A:4, B:10)? Why not just throw away the sample for the non-matching method or even just sample them separately? It isn't a bivariate distribution so each method's draws are independent of the other method's.

it is very interesting ! how to import data from other software in to winbgs for analysis

Please Please make a video on Gaussian Process.....none of the video on the youtube gives intuition like your videos

really really good

Can we use the posterior of one round of computation as the informative prior of the next round of improved estimate? When it has no relation to reality such as an expert opinion, and still has its origin from a wild guess of uniform distribution, would it be a better estimate?

Hi, thanks the great talk. May I have a question that do I need to follow the order of rateA and rateB when computing the diff, or I can randomly draw the value from A and B to compute the diff?

Unable to find part 3. Could you help me with it?

do you realize that your Nordic accent is very sexy?

Rasmus, you should really start making videos again. Seriously, the way you teach is, by far, one of the best I've seen on RU-vid (and I watched plenties of videos here since I am a self-taught student of economics). I am amazed how tricky and deep concepts seem so simple when brought by you. Thank you for this series on Bayesian Statistics.

Thank you for the excellent introduction series! Shouldn’t the profit calculation at 17:58 also take into consideration the cost of the campaigns for the non-respondents? So it would be profitA = (rateA*(1000-30))+(1-rateA)*-30. And then profitB = (rateB*(1000-300))+(1-rateB)*-300

One question why don't you keep t=268 the 72% rate2 for model B but throw both tries away?

Really like your 3 part introduction on Bayesian modelling! Clearly structured, focussed and entertaining - thank you!

"How much we should trust our CEO? I think he is smoking tobacco, but I don't know"

Do you make the slides available? Such great explanations. Love your presentations. Thank you so much 🙏

Great videos!!! Thank you very much. I have a question though that troubles me. I didn't understand why, while calculating the posteriors for both A and B methods we have to take the one in consideration with the other. Why do we need to keep the probability values only when both model responses agree with the observed responses. Couldn't we produce the posteriors by running the models independently?

Answer to the question "Why to accept both A & B at the same time": I think got this one :). for both A & B you need a normalizer often call the evidence or the marginal likelihood P(data) So it must be proportional and the same for A & B , thats why when you want to compare A and B you must accept both. It is an evidence for the joint distribution of A & B

17:30 should be profitB = rateB x 1000 - (300 + 30)

Is there a R tutorial for this video?

¡Breathtaking! ¡Bravo! No words,just thank you for sharing.

Great introduction of Bayesian thinking. Much clearer than textbooks.

Thank you Rasmus! A very clear and accessible introduction with lots of opportunties to actually apply what we learned. Thank you for all you work on this.

17:34 Method B: Don't you send out a brochure (30 Kr) with a salmon (300 Kr) unless it is already included (Fish 270 Kr Brochure 30 Kr). Don't forget to include a QR code. :)

Notes for my future revision. Why Bayesian Data Analysis? 0:29 How easy it is to change Bayesian model while the computation stay the same. 0:32 You have great flexibility when building Bayesian models, and can focus on that, rather than computational (algorithmic) issues. There are often computational (processing) issue in fitting Bayesian model. But since there is clean separation between specifying and fitting model in a bayesian framework, you often don't have to focus too much on how your model is computed when you construct it. That mean you can focus on what assumptions are reasonable and what information you should use, rather than on algorithm when doing the actual modelling. There are many tools to help fitting Bayesian models (Stan, PyMc), just specifying the model might just be enough.

At 11:50 you say the CEO suggested the rate of sign up is usually between 5% and 50%. But a little after 9:12, he was quoted as saying 'between 5% and 15%'. I guess the accuracy does not matter so much. You are perhaps interested in getting closer than the initial model with uniform distribution.

7:50 I am confused with rate_diff. Simulated subscription rates for Method A and B are not related in any way other than the order in which these are generated (cf. unlike repeated mesures pre- and post-test scores). Is it meaningful to calculate difference scores between two numbers that are not related? Can we just look at confidence intervals?

Finally Bayesian is making sense

All three parts are super helpful. Thanks a lot!

Great video! For the decision analysis, why do you need bayesian analysis? You could just use the maximal likelihood estimation with the cost equation and see that the brochure alone is better. (0.38*1000 - 30 > 0.63*1000 - 300). If you're making a decision based off of this, it doesn't seem beneficial to do the bayesian analysis.

on 17:57, I think the Profit B should be =rateB *1000 - (300 for salmon +30 for brochure)

Very good explanation. Shouldn't profitB = rateB*1000-300-30?

Awesome video! But I think in the method B the cost of the brochure is missing, it's only been considered the cost of salmon, so the profitB would be (1000rateB-330) instead of (1000rateB-300), Anyway, the whole idea is perfectly explained. Cheers!

@rasmusab, there is a small mistake in your tutorial at @4:40. The second rate is not incorporated into the model so 64 =/= 72 (which was from the previous draw).

Great lecture

Great introduction, thank you!

Congratulations! Perfect video and brilliant explanation! I wonder, what's wrong with your BayesianAid project? I missed it on cran.r-project.org?! In fact I use it and even have made a little fix for RNG.

Thanks for posting these videos, man. In a economics student and I am very interested in this kind of thing.

Very clear and helpful! Best resources I've seen now.

Thanks for the upload, your explanations are great. Just FYI, during the 13th minute, your x axis label on the "Informative" histogram might be a mistake? It caused a little confusion for me.

Shouldn't we deduct 330 from expected profit of B since we are sending both salmon and a pamphlet?

very informative. Many Thanks

Thanks for the great videos! Learnt a lot

Tack Rasmus, javligt bra!

Simply great!

hail papous with tsimpouk

really like the practical example

Excellent content!

Nice work!

Please answer a question. How to generate Informative rates using beta(3,25) for n draws?

Thanks

when will part three be posted?? these videos are good!

Thanks!

9:20 my man