Bayesian
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.
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 Variables
The prior probability of the event. At any point in time (ignoring all external influences) how likely is this event to occur?
The probability at which the sensor should trigger to on
.
The observations which should influence the likelihood that the given event has occurred.
The supported platforms are state
, numeric_state
, and template
. They are modeled after their corresponding triggers for automations, requiring to_state
(for state
), below
and/or above
(for numeric_state
) and value_template
(for template
).
Defines the template to be used. Required for template
.
The probability of the observation occurring, given the event is true
.
The probability of the observation occurring, given the event is false
can be set as well.
1 - prob_given_true
if prob_given_false
is not set
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:
- platform: "state"
entity_id: "sensor.living_room_motion"
prob_given_true: 0.4
prob_given_false: 0.2
to_state: "off"
- platform: "state"
entity_id: "sensor.basement_motion"
prob_given_true: 0.5
prob_given_false: 0.4
to_state: "off"
- platform: "state"
entity_id: "sensor.bedroom_motion"
prob_given_true: 0.5
to_state: "on"
- platform: "state"
entity_id: "sun.sun"
prob_given_true: 0.7
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:
- platform: "numeric_state"
entity_id: "sensor.outside_air_temperature_fahrenheit"
prob_given_true: 0.95
below: 50
Finally, here’s an example for template
observation platform, as seen in the configuration it requires value_template
.
# Example configuration.yaml entry
binary_sensor:
name: "Paulus Home"
platform: "bayesian"
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: 0.95