Bayesian

The bayesian binary sensor platform observes the state from multiple sensors. It uses Bayes’ rule to estimate the probability that an event is occurring given the state of the observed sensors. If the estimated posterior probability is above the probability_threshold, the sensor is on; otherwise, it is off.

This allows for the detection of complex events that may not be readily observable, e.g., cooking, showering, in bed, the start of a morning routine, etc. It can also be used to gain greater confidence about events that are directly observable, but for which the sensors can be unreliable, e.g., presence.

Theory

A key concept in Bayes’ Rule is the difference between the probability of the ‘event given the observation’ and the probability of the ‘observation given the event’. In some cases, these probabilities will be similar. The probability that someone is in the room given that motion is detected is similar to the probability motion is detected given that someone is in the room when motion sensors are accurate. In other cases, the distinction is much more important. The probability one has just arrived home (the event) each time the front door contact sensor reports open (the observation) (p=0.2) is not the same as the probability the front door contact sensor reports open (the observation) when one comes home (the event) (p=0.999). This difference is because one opens the door several times a day for other purposes.

In the configuration use the probability of the observation (the sensor state in question) given the event (the assumed state of the Bayesian binary_sensor).

Estimating probabilities

1. Avoid 0 and 1, these will mess with the odds and are rarely true - sensors fail.
2. When using 0.99 and 0.001. The number of 9s and 0s matters.
3. Most probabilities will be time-based - the fraction of time something is true is also the probability it will be true.
4. Use your Home Assistant history to help estimate the probabilities.
   • prob_given_true: - Select the sensor in question over a time range when you think the bayesian sensor should have been true. prob_given_true: is the fraction of the time the sensor was in to_state:.
   • prob_given_false: - Select the sensor in question over a time range when you think the bayesian sensor should have been false. prob_given_false: is the fraction of the time the sensor was in to_state:.
5. Don’t work backwards by tweaking prob_given_true: and prob_given_false: to give the results and behaviors you want, use #4 to try and get probabilities as close to the ‘truth’ as you can, if your behavior is not as expected consider adding more sensors or see #6.
6. If your Bayesian sensor ends up triggering on too easily, re-check that the probabilities set and estimated make sense, then consider increasing probability_threshold: and vice-versa.

Configuration

To enable the Bayesian sensor, add the following lines to your configuration.yamlThe configuration.yaml file is the main configuration file for Home Assistant. It lists the integrations to be loaded and their specific configurations. In some cases, the configuration needs to be edited manually directly in the configuration.yaml file. Most integrations can be configured in the UI. [Learn more] file. After changing the configuration.yamlThe configuration.yaml file is the main configuration file for Home Assistant. It lists the integrations to be loaded and their specific configurations. In some cases, the configuration needs to be edited manually directly in the configuration.yaml file. Most integrations can be configured in the UI. [Learn more] file, restart Home Assistant to apply the changes.

# Example configuration.yaml entry
binary_sensor:
  - platform: bayesian
    name: "Kitchen Occupied"
    prior: 0.3
    probability_threshold: 0.5
    observations:
      - entity_id: "switch.kitchen_lights"
        prob_given_true: 0.6
        prob_given_false: 0.2
        platform: "state"
        to_state: "on"

Full examples

The following is an example for the state observation platform.

# Example configuration.yaml entry
binary_sensor:
  platform: "bayesian"
  name: "in_bed"
  unique_id: "172b6ef1-e37e-4f04-8d64-891e84c02b43" # generated on https://www.uuidgenerator.net/
  prior: 0.25 # I spend 6 hours a day in bed 6hr/24hr is 0.25 
  probability_threshold: 0.8 # I am going to be using this sensor to turn out the lights so I only want to to activate when I am sure
  observations:
    - platform: "state"
      entity_id: "sensor.living_room_motion"
      prob_given_true: 0.05 # If I am in bed then I shouldn't be in the living room, very occasionally I have guests, however
      prob_given_false: 0.2 # My sensor history shows If I am not in bed I spend about a fifth of my time in the living room
      to_state: "on"
    - platform: "state"
      entity_id: "sensor.basement_motion"
      prob_given_true: 0.5 # My sensor history shows, when I am in bed, my basement motion sensor is active about half the time because of my cat
      prob_given_false: 0.3 # As above but my cat tends to spend more time upstairs or outside when I am awake and I rarely use the basement
      to_state: "on"
    - platform: "state"
      entity_id: "sensor.bedroom_motion"
      prob_given_true: 0.5 # My sensor history shows when I am in bed the sensor picks me up about half the time
      prob_given_false: 0.1 # My sensor history shows I spend about 10% of my waking hours in my bedroom
      to_state: "on"
    - platform: "state"
      entity_id: "sun.sun"
      prob_given_true: 0.7 # If I am in bed then there is a good chance the sun will be down, but in the summer mornings I may still be in bed
      prob_given_false: 0.45 # If I am am awake then there is a reasonable chance the sun will be below the horizon - especially in winter
      to_state: "below_horizon"
    - platform: "state"
      entity_id: "sensor.android_charger_type"
      prob_given_true: 0.95 # When I am in bed, I nearly always plug my phone in to charge
      prob_given_false: 0.1 # When I am awake, I occasionally AC charge my phone
      to_state: "ac"

Next up an example which targets the numeric_state observation platform, as seen in the configuration it requires below and/or above instead of to_state.

# Example configuration.yaml entry
binary_sensor:
  name: "Heat On"
  platform: "bayesian"
  prior: 0.2
  probability_threshold: 0.9
  observations:
    - platform: "numeric_state"
      entity_id: "sensor.outside_air_temperature_fahrenheit"
      prob_given_true: 0.95
      prob_given_false: 0.05
      below: 50

Finally, here’s an example for template observation platform, as seen in the configuration it requires value_template. This template will evaluate to true if the device tracker device_tracker.paulus shows not_home and it last changed its status more than 5 minutes ago.

# Example configuration.yaml entry
binary_sensor:
  name: "Paulus Home"
  platform: "bayesian"
  device_class: "presence"
  prior: 0.5
  probability_threshold: 0.9
  observations:
    - platform: template
      value_template: >
        {{is_state('device_tracker.paulus','not_home') and ((as_timestamp(now()) - as_timestamp(states.device_tracker.paulus.last_changed)) > 300)}}
      prob_given_true: 0.05
      prob_given_false: 0.99