home-assistant.io/source/_components/binary_sensor.bayesian.markdown

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page	Bayesian Binary Sensor	Instructions on how to integrate threshold Bayesian sensors into Home Assistant.	2017-08-27 20:05	true	false	true	true	home-assistant.png	Utility	Local Polling	0.53	internal

The bayesian binary sensor platform observes the state from multiple sensors and uses Bayes' rule to estimate the probability that an event has occurred 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.

{% linkable_title Configuration %}

To enable the Bayesian sensor, add the following lines to your configuration.yaml:

# Example configuration.yaml entry
binary_sensor:
  - platform: bayesian
    prior: 0.1
    observations:
      - entity_id: 'switch.kitchen_lights'
        prob_given_true: 0.6
        prob_given_false: 0.2
        platform: 'state'
        to_state: 'on'

{% configuration %} prior: description: > The prior probability of the event. At any point in time (ignoring all external influences) how likely is this event to occur? required: true type: float probability_threshold: description: The probability at which the sensor should trigger to on. required: false type: float default: 0.5 name: description: Name of the sensor to use in the frontend. required: false type: string default: Bayesian Binary Sensor observations: description: The observations which should influence the likelihood that the given event has occurred. required: true type: list keys: entity_id: description: Name of the entity to monitor. required: true type: string prob_given_true: description: The probability of the observation occurring, given the event is true. required: true type: float prob_given_false: description: The probability of the observation occurring, given the event is false can be set as well. required: false type: float default: "1 - prob_given_true if prob_given_false is not set" platform: description: > The only supported observation platforms are state and numeric_state, which are modeled after their corresponding triggers for automations, requiring below and/or above instead of to_state. required: true type: string to_state: description: The target state. required: true type: string {% endconfiguration %}

{% linkable_title Full examples %}

The following is an example for the state observation platform.

# Example configuration.yaml entry
binary_sensor:
  name: 'in_bed'
  platform: 'bayesian'
  prior: 0.25
  probability_threshold: 0.95
  observations:
    - entity_id: 'sensor.living_room_motion'
      prob_given_true: 0.4
      prob_given_false: 0.2
      platform: 'state'
      to_state: 'off'
    - entity_id: 'sensor.basement_motion'
      prob_given_true: 0.5
      prob_given_false: 0.4
      platform: 'state'
      to_state: 'off'
    - entity_id: 'sensor.bedroom_motion'
      prob_given_true: 0.5
      platform: 'state'
      to_state: 'on'
    - entity_id: 'sun.sun'
      prob_given_true: 0.7
      platform: 'state'
      to_state: 'below_horizon'

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:
    - entity_id: 'sensor.outside_air_temperature_fahrenheit'
      prob_given_true: 0.95
      platform: 'numeric_state'
      below: 50