Hello! This is my first time posting on this forum so please go easy on me :).
I’m trying to understand the right way to describe a one-hot kind of model.
Each output datapoints is a vector of independent measurements. Every non-zero feature for every datapoint is either signal or noise, and there can only be 1 signal feature and the rest are noise.
I am assuming that the signal comes from a triangular distribution and the noise comes from a gamma distribution.
I’m trying to figure out how to specify a model for this (one latent feature comes from one distribution and all others come from the other distribution). Would this be something like a logistic representation?
The next step will be to turn this into a mixture model. I’ll be at StanCon this year and I want to get a ways into constructing my model so I can get more out of the tutorials.
[Please include Stan program and accompanying data if possible]example_data.csv (123.0 KB)