Neural Two-Separator Likelihood

Constructs a simple 3-category likelihood model based on a latent vector z. The likelihood is defined by two logistic separators:

Usage

makeneurolik(a = 0.3)

Arguments

a

Separation parameter controlling the spacing between the two logits.

Value

A function mapping a latent vector z to a probability vector.

Details

p1 = sigmoid(mean(z) - a) p2 = sigmoid(mean(z) + a)

producing a 3-class probability vector:

(1 - p1, p1 - p2, p2)

This likelihood is useful for toy neural classification models or simple latent-to-categorical mappings.