Interesting references for Model types

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Hello Neuromatchers!

I’m seeing a lot of good activity in pods exchanging interesting links and references for the day. Let’s let everybody benefit from this by posting in this public category - other interactives and observers will be able to see this too. I’ll start - this resource on information theory comes from @Nishant over in vivacious-giraffe:

So I was doing this coursera course on computational neuroscience and Rich Pang from Adrienne Fairhall’s lab explained entropy and information theory really well (at least for someone with a non-mathematical background.)
https://www.coursera.org/learn/computational-neuroscience/lecture/9ZTj6/whats-up-with-entropy-by-rich-pang
https://www.coursera.org/learn/computational-neuroscience/lecture/KEUwL/information-theory-thats-crazy-by-rich-pang

What are some good references for the day?

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Some materials on why the exponential distribution is the maximum entropy distribution under a fixed mean constraint.

Friendly introduction with other constraint examples: https://sgfin.github.io/2017/03/16/Deriving-probability-distributions-using-the-Principle-of-Maximum-Entropy/#3-derivation-of-maximum-entropy-probability-distribution-of-half-bounded-random-variable-with-fixed-mean-barr-exponential-distribution

Formal treatment (Theorem 6.7): https://web.stanford.edu/class/stats311/Lectures/lec-07.pdf

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Note that for @pmin 's links, you must log in to coursera and enroll in the course (free) for links to work

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guys we please do this for every day…

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@sanil.4281222 start a new thread with a few links!

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thanks but i do not have any resource link. i was interested in the links myself