Yesterday at 6:14 PM, my girlfriend replied “okay” to a message that would ordinarily receive either “okayyy” or a heart reaction. My intuition suggested she was mad. Intuition is notoriously unreliable, so I built a model.

Let M denote the event that my girlfriend is mad at me. I begin with a prior: P(M) = 0.18, estimated from the previous 90 days.

Evidence:

E1 = "I'm fine."
P(E1 | M) = 0.71
P(E1 | ¬M) = 0.19
Posterior after E1: P(M | E1) ≈ 0.45

E2 = "do whatever you want"
P(E2 | M) = 0.64
P(E2 | ¬M) = 0.09
Naively, the posterior rises sharply; however, E1 and E2 are obviously not conditionally independent.

The main difficulty is that “I’m fine” and "do whatever you want” appear to be downstream of the same latent annoyance state. Treating them as independent would double-count evidence.

I therefore model the following latent states:

  1. M0 = not mad
  2. M1 = mildly annoyed
  3. M2 = mad about a specific thing
  4. M3 = mad that I don’t know what she’s mad about
  5. M4 = mad that I am doing Bayesian inference instead of asking

At 9:02 PM I finally asked, “Are you mad at me?” She replied, “No, but I am now because you’ve been acting weird all night.” This is strong evidence that the act of inference altered the system being measured.