From d68f9281614af78f259b03a5bcd834ac1670b7c8 Mon Sep 17 00:00:00 2001
From: Fabian Affolter <fabian@affolter-engineering.ch>
Date: Wed, 30 Aug 2017 20:55:59 +0200
Subject: [PATCH] Remove line breaks and create list of configuration vars

---
 .../binary_sensor.bayesian.markdown           | 73 ++++++++-----------
 1 file changed, 29 insertions(+), 44 deletions(-)

diff --git a/source/_components/binary_sensor.bayesian.markdown b/source/_components/binary_sensor.bayesian.markdown
index 43b508e60a9..588798a355a 100644
--- a/source/_components/binary_sensor.bayesian.markdown
+++ b/source/_components/binary_sensor.bayesian.markdown
@@ -14,42 +14,44 @@ 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 `probabiliy_threshold`, the value of the sensor is `on`.
+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 `probabiliy_threshold`, the value of the sensor is `on`.
 Otherwise, the sensor is `off`.
 
-This allows for the detection of complex events that may not be readily
-observable, i.e. cooking, showering, in bed, start of 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, i.e. presence.
+This allows for the detection of complex events that may not be readily observable, i.e., 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, i.e., presence.
 
 To enable the Bayesian sensor, add the following lines to your `configuration.yaml`:
 
 ```yaml
 # Example configuration.yaml entry
 binary_sensor:
-  name: 'currently_cooking'
-  platform: 'bayesian'
-  prior: 0.1
-  probability_threshold: 0.7
-  observations:
-    - entity_id: 'switch.kitchen_lights'
-      prob_given_true: 0.6
-      prob_given_false: 0.2
-      platform: 'state'
-      to_state: 'on'
-    - entity_id: 'sensor.stove_temperature'
-      prob_given_true: 0.9
-      platform: 'numeric_state'
-      above: 100
-    - entity_id: 'sensor.kitchen_motion'
-      prob_given_true: 0.5
-      prob_given_false: 0.2
-      platform: 'state'
-      to_state: 'on'
+  - 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'
@@ -76,20 +78,3 @@ binary_sensor:
       to_state: 'below_horizon'
 ```
 
-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** (*Required*): The observations which should influence the
-likelihood that the given event has occurred. The only supported observation
-platforms are `state` and `numeric_state`, which are modeled after their
-corresponding triggers for automations.  In addition to the automation syntax,
-the observations also require `prob_given_true` (the probability of the
-observation occurring, given the event is true).  The optional
-`prob_given_false` (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`.
-- **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`.
-