---
layout: page
title: "Bayesian Binary Sensor"
description: "Instructions how to integrate threshold Bayesian sensors into Home Assistant."
date: 2017-08-27 20:05
sidebar: true
comments: false
sharing: true
footer: true
logo: home-assistant.png
ha_category: Binary Sensor
ha_iot_class: "Local Polling"
ha_release: 0.53
---


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.

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

```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?
- **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.  If `prob_given_false` is not set, it will default to `1 - prob_given_true`.
  - **platform** (*Required*): The only supported observation platforms are `state` and `numeric_state`, which are modeled after their corresponding triggers for automations.
  - **to_state** (*Required*): The target start.
- **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`.

## {% linkable_title Full examples %}

```yaml
# 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: 'sensor.sun'
      prob_given_true: 0.7
      platform: 'state'
      to_state: 'below_horizon'
```