Bayesian Binary Sensor
The bayesian
binary sensor platform observes the state from multiple sensors and uses Bayesâ€™ 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.
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:
 prior (Required): The prior probability of the event. At any point in time (ignoring all external influences) how likely is this event to occur?

probability_threshold (Optional): The probability at which the sensor should trigger to
on
. 
name (Optional): Name of the sensor to use in the frontend. Defaults to
Bayesian Binary sensor
. 
observations array (Required): The observations which should influence the likelihood that the given event has occurred.
 entity_id (Required): Name of the entity to monitor.

prob_given_true (Required): The probability of the observation occurring, given the event is
true
. 
prob_given_false (Optional): The probability of the observation occurring, given the event is
false
can be set as well. Ifprob_given_false
is not set, it will default to1  prob_given_true
. 
platform (Required): The only supported observation platforms are
state
andnumeric_state
, which are modeled after their corresponding triggers for automations, requiringbelow
and/orabove
instead ofto_state
.  to_state (Required): The target state.
Full examples
# 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'
# 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