Jev and the Bayesian sensor

Home Assistant already has a sensor that turns several readings into one probability, the Bayesian binary sensor. Both give you a number from 0 to 1 and a threshold that turns it into on or off. They get the number in different ways, and that decides which one fits.

How each gets its number

The Bayesian sensor multiplies probabilities you give it. You set a prior, and for each observation you say how often it is seen when the answer is yes (prob_given_true) and when it is no (prob_given_false). An observation is a state, a numeric range or a template. The sensor combines them with Bayes' rule and treats them as independent of each other.

Jev reads the states and your question in words, and the model gives the probability. You write no probabilities. You write what the question means, and optionally what counts as yes and as no.

Side by side

	Bayesian sensor	Jev noul
You supply	a prior and two probabilities per observation	a question in words
Where it runs	in Home Assistant	a call to the TypeSafe API
Cost	nothing	input tokens, see what it costs
Time to update	when a state changes	250 to 580 ms warm, measured from the Netherlands
Same input, same output	yes	no. One sentence gave 0.25, 0.28 and 0.31 in three runs
Reads free text	only through a template you write	yes, a note or a transcript is ordinary input
Observations that depend on each other	counted as independent, so they count twice	read together
Why it said what it said	each observation's share is visible	a number with no reasoning

Which to use

Use the Bayesian sensor when you know, or can measure, how often each reading goes with the answer, and the readings are few and mostly independent. "Somebody is home" from a phone, a door and motion in the hall is the textbook case. It costs nothing, it works without the internet, and you can see why it changed.

Use Jev when the probabilities are the part you cannot write down. A washing machine whose power draw rises and falls through a programme, a doorbell transcript, or three readings that only mean something together are hard to express as prob_given_true. Jev takes them as they are.

If you already have a working Bayesian sensor, keep it. Jev does not make one wrong.

Using both

A Jev noul sensor is a number, so a Bayesian sensor can use it as a numeric_state observation, next to readings that it handles well on its own:

binary_sensor:
  - platform: bayesian
    name: Laundry waiting
    prior: 0.2
    probability_threshold: 0.8
    observations:
      - platform: numeric_state
        entity_id: sensor.jev_laundry_done
        above: 0.6
        prob_given_true: 0.9
        prob_given_false: 0.1
      - platform: state
        entity_id: binary_sensor.laundry_door
        to_state: "off"
        prob_given_true: 0.95
        prob_given_false: 0.5

The probabilities in that example are placeholders. Measure your own, for example with jev.calibrate, which reports precision and recall for a Jev sensor against an entity that shows what was really true.