Tutorial Session: Variational Bayes and Beyond: Bayesian Inference for Big Data

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[@AS-nn4bv]

technical content starts at 17:10

[@sijingzhong]

What is the meaning of no closed form in step 2 of Bayesian inference?

[@deepaihub4589]

thank you for much for this amazing lecture

[@sanjaykrish8719]

Wow. Amazing lecture.. so much intuition and clarity

[@SrikantGadicherla]

Starts at 7:02

[@mattgosden]

Brilliant. Great teacher.

[@feymanknowledge6786]

This is very good lecture, I liked. But audio is so asynchronized, so I had to download the video to manually adjust.

[@yeshuang2226]

Excellent lecture. However, the video resolution is not good.

[@gaspell]

Thank you

[@SrikantGadicherla]

1:24:58 Excellent catch there... we assume independence of data *GIVEN* the parameters. If the data is independent what the heck are we doing here.

[@georgehuang8023]

We are doing for the identity distribution of data, independence of instances in data is NOT equivalent to independence of predictor(input) and response(output).

[@vadimborisov4824]

fantastic lecture

[@malharjajoo7393]

Good explanation, but why is this method called "variational" Bayes ?

[@SuperMontyrock]

it comes from calculus of variation

[@ds5375]

Because we are approximating the real but intractable distribution p from distribution family P, by optimizing a simpler but tractable distribution q from family Q. We a therefore varying the distribution from P to Q for the purpose of approximation.