The Unreasonable Effectiveness of Bayesian Prediction

[@CodeEmporium]

We need a "Ritvik marker catching intro compilation"

[@ritvikmath]

🤣 good idea!

[@kaanbcakc8168]

Your explanations about Bayesian concepts are very clear, keep it up! :)

[@nmtsmea]

Isn’t the posterior the probability of beta given data? It’s contradictory to the other video you made.

[@chiawen.]

Your explanations are fantastic! I think Bayesian Optimization would be a nice topic as well! Could you make a video about that? :D

[@abhishek50393]

Great vid, you should do a full series of Bayesian methods

[@joelrubinson9973]

very interesting. relevant to my work on creating uber models of advertising effectiveness where the signals come from different walled gardens and where the A/B test results can vary for the same publisher (e.g. Facebook) for the same brand across camigpans. True finding or statistical variation that can be 'shrunk' by prior distributions on the parameters in the uber model?

[@victorviana4012]

Great Video!!!! Do you know a study reference for code implementation of this concept?

[@TheBestTuber396]

Can you do a video on how to understand power laws

[@ChocolateMilkCultLeader]

Fantastic

[@Stem667]

Would the diversity not only matter when the likelihood function has multiple local minima? Otherwise, why "hedge our bets" if we are likelihood-maximising values of beta are all very similar?

[@sabinewien2665]

Hi Great Video 😊 I work on my thesis in Electro Engineering and your Movie helped me a lot. Can you please provide me with your sources so I can use them too? Thank you very much!

[@kisholoymukherjee]

Would be great if you can make some videos on the use of Bayesian approach for Marketing Mix Modelling

[@Darkev77]

What does he exactly mean in 9:14 by “we will sample a new beta vector from that posterior distribution”? Someone clarify please?

[@hristovassilev7812]

It means you you „learn“ the distribution of beta from the data. You can then sample that distribution(e.g. with MCMC). If the variance of the samples is high this means the model is uncertain about its prediction. That’s my understanding at least.

[@Darkev77]

@@hristovassilev7812 I thought that after maximum likelihood estimation, you get a single set of weights and bias (B0 and B1) that maximize the probability of observing your data. So how does that turn into a "distribution"; when did you generate that distribution where you can sample a "new beta vector" from? Thanks!

[@hristovassilev7812]

@@Darkev77 What you describe is correct for maximum likelihood estimation. But since the video refers to a Bayesian method of estimating the parameters the idea is a bit different: you treat B0 and B1 themselves as a random variable. You generate the distribution of B0 and B1 using Bayes theorem: p(B | data) = p(data | B) * p(B) / p(data)

[@chenqu773]

This video solves me a confusion around MLE and bayesian, by that magic p(β)=1. Thank you man!

[@mathematicalninja2756]

They asked me this today in flipkart interview.

[@chadgregory9037]

I feel like tensorflow probabilities is such a huge deal, like omg a huge deal

[@OwenMcKinley]

If I had the ability, I'd award you a Nobel prize

[@taotaotan5671]

I think the posterior expectation may be in the sweet spot.

[@robertbarta2793]

Super explanation.

[@djlinux64]

How do you measure model performance if you are changing models every day?

[@juneyang6534]

Perhaps we can think of the evolving models as one dynamic model and check the performance on a daily basis？

[@rsilveira79]

Very clear explanations

[@hameddadgour]

Great explanation!

[@ericostring8182]

This is awesome stuff

[@klam77]

Excellent!

[@sharmilakarumuri6050]

Clearly explained , awesome.....need more videos on bayesian stats

[@geoffreyanderson4719]

Ritvikmath: Building on your nice ideas.... Q1 - Is there a jupyter notebook of this Bayes sampling model yet? It's nice concept. Q2 - It was found that recency of purchases (which you explicitly incorporated) and frequency of purchases too are both predictive of churn, in Univ of Virgina's Marketing Analytics course. Let's make a ML model using Bayes sampling, but now incorporating both these factors. Yet another AI research direction stares at us in the face....

[@johnpalmer8538]

Absolutely incredible video. Your explanation of concepts is crystal clear and easy to follow. Amazing job man :)

[@ElbertMaata-cc2uq]

Kindly make the theme dark so that the illustrations get cleared . Thank you.