Constructs a simple 3-category likelihood model based on a latent vector z.
The likelihood is defined by two logistic separators:
makeneurolik(a = 0.3)A function mapping a latent vector z to a probability vector.
Separation parameter controlling the spacing between the two logits.
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.