Density estimation with normalizing flow in a minute

Normalizing flow is a generative deep neural network which can output a probability density function describing your data, by training a series of tunable transforms in the form of a neural network.

In the future, this will help in connecting simulations which we do not know how to write down an analytical likelihood with real-world data, hence opening up an avenue to constraining real-world phenomenon with simulations directly.

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[arvid]

I've tried to grasp this concept for a while now, and now I finally get it - thank you!

[orjihvy]

This is a really good intuitive explanation.

[katequark]

super cool 👍thanks for the explanation

[TragicGFuel]

so is this what's happening when generative models say they are predicting by sampling a probability distribution?
does that mean the machine learning model is essentially learning the mean and standard deviation of the data?

[hajvklkaj]

But how does it works for one variable? The flow must be monotonic function and thus will not give us many maxima and minima as seen here.

[MathStuff]

Your first link with to your paper (2007.10350) is broken