I have been reading a lot about var. inference, yet every case ends with terms and derivations, what I miss is a broad picture of a problem. Please confirm if I understand correctly:
Let's assume I live in a house with a dog. My dog barks, sometimes for no reason, but mostly if someone is in front of the house. As I don't know if someone is in front of the house, I use data from my neighbour who calls me once per day and and tells me if she saw any people near to my house.
And this is where inference comes to the game.
My model is:
P(X) = someone in front of the house
P(Z) = Dog is barking
Q(Z|X) = Neighbour's data about people in front of my house when dog was barking
I would like to know:
So here comes the KL divergence:
Which basically mean, do relative distance of neighbour's data when she saw a person, given dog barking vs. my observation of dog barking and people being in front of the house. Is this it, is my explanation correct?