(ML 10.1) Bayesian Linear Regression

[@JReuben111]

This vid makes references to watching other vids before proceeding to the next one !

[@鄭伯俞]

Amazing explanation! Save my ML homework, thank you so much

[@alexandrospapangelou115]

Hi there. Thanks for the videos mathematicalmonk; they are very informative. I watched the ML10 series and the only thing that bugs me is the assumption that a,b are known. I have multivariate data which I want to model with a regression model and I am interested not only in finding yi but also the variance. Can someone please direct me to a tutorial or is there a way to infer the above values?

[@Epistemophilos]

Great stuff, thanks for the effort!

[@muzaffergurersalan8529]

How can we assume a,b are known?

[@ProgrammingTime]

Damnit, your videos rule

[@DachuanHuang]

When reached 5:44, I have a question: in MLE, we assumed $Y$ follows Gaussian distribution right? this is not enough?

[@jiatianxu757]

I think u are right. Here monk didn't explain very clearly. In fact, MLE can also give us P(y|x, D). And in MLE, we choose our basis function by optimizing the loss function, which he mentioned in previous videos. So, I think the reason why we choose Bayesian is not so correct in this video.

[@mingjiazhang2712]

great thanks. I am quite new in this field. May I know why all w have mean of zero? It seems to me these coefficients should have non zero means.

[@yleswill3053]

我也不明白，😔

[@keno2055]

5 years passed by and no one knows the answer ;(

[@jl2696]

@@keno2055 This video was made over 10 years ago and has very few views. That's probably why.

[@tommys4809]

Making prior assumprion about the distribution of theta being standard normal N(0,1). Common prior assumption

[@handdancin]

excellent! dont sell bayesian models short though! they are good for many more reasons :)

[@orrymr]

How do the MLE estimates represent our uncertainty?

[@junweima]

it doesn't. bayesian does.