I’m building up a bayesian sensor to show a light when it’s a great time to go for a walk using outdoor weather observations

I have a question about how ‘above’ and ‘below’ work (for my example let’s just use ‘temperature’)

Say I have the following observations

```
- platform: "numeric_state"
entity_id: "sensor.openweathermap_feels_like_temperature"
prob_given_true: 0.9
prob_given_false: 0.15
above: 65
- platform: "numeric_state"
entity_id: "sensor.openweathermap_feels_like_temperature"
prob_given_true: 0.7
prob_given_false: 0.15
above: 75
below: 85
```

In the first observation, do I *need* to specify `below: 75`

, does the second observation override that? or are they cumulative.

So, for example, if the temperature is 80, is the probability 0.8?

Update: Yes, but it’s not that simple.

I found this, which is AWESOME for working on bayesian sensors

https://bayesian-calculator.greenleafimaging.com/