From 909f08e8551f0a7c3718ac1faa8b5dd37de4c097 Mon Sep 17 00:00:00 2001
From: HarvsG <11440490+HarvsG@users.noreply.github.com>
Date: Mon, 3 Oct 2022 12:26:56 +0000
Subject: [PATCH] prob_given_false is soon req, show in examples (#24276)

---
 source/_integrations/bayesian.markdown | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/source/_integrations/bayesian.markdown b/source/_integrations/bayesian.markdown
index 472ca1f07c0..035a235533b 100644
--- a/source/_integrations/bayesian.markdown
+++ b/source/_integrations/bayesian.markdown
@@ -31,7 +31,7 @@ In the configuration use the probability of the observation (the sensor state in
 4. Use your Home Assistant history to help estimate the probabilities.
    - `prob_given_true:` - Select the sensor in question over a time range when you think the `bayesian` sensor should have been `true`. `prob_given_true:` is the fraction of the time the sensor was in `to_state:`.
    - `prob_given_false:` - Select the sensor in question over a time range when you think the `bayesian` sensor should have been `false`. `prob_given_false:` is the fraction of the time the sensor was in `to_state:`.
-5. Don't work backwards by tweaking `prob_given_true:` and `prob_given_false:` to give the results and behaviors you want, use #4 to try and get probabilities as close to the 'truth' as you can, if your behavior is not as expected consider adding more sensors or see #7.
+5. Don't work backwards by tweaking `prob_given_true:` and `prob_given_false:` to give the results and behaviors you want, use #4 to try and get probabilities as close to the 'truth' as you can, if your behavior is not as expected consider adding more sensors or see #6.
 6. If your Bayesian sensor ends up triggering `on` too easily, re-check that the probabilities set and estimated make sense, then consider increasing `probability_threshold:` and vice-versa.
 
 ## Configuration
@@ -101,7 +101,7 @@ observations:
       required: true
       type: float
     prob_given_false:
-      description: Assuming the bayesian binary_sensor is `false` the probability of this entity state is occurring.
+      description: Assuming the bayesian binary_sensor is `false` the probability the entity state is occurring.
       required: true
       type: float
 {% endconfiguration %}
@@ -122,12 +122,12 @@ binary_sensor:
       entity_id: "sensor.living_room_motion"
       prob_given_true: 0.05 # If I am in bed then I shouldn't be in the living room, very occasionally I have guests, however
       prob_given_false: 0.2 # My sensor history shows If I am not in bed I spend about a fifth of my time in the living room
-      to_state: "off"
+      to_state: "on"
     - platform: "state"
       entity_id: "sensor.basement_motion"
       prob_given_true: 0.5 # My sensor history shows, when I am in bed, my basement motion sensor is active about half the time because of my cat
       prob_given_false: 0.3 # As above but my cat tends to spend more time upstairs or outside when I am awake and I rarely use the basement
-      to_state: "off"
+      to_state: "on"
     - platform: "state"
       entity_id: "sensor.bedroom_motion"
       prob_given_true: 0.5 # My sensor history shows when I am in bed the sensor picks me up about half the time
@@ -136,6 +136,7 @@ binary_sensor:
     - platform: "state"
       entity_id: "sun.sun"
       prob_given_true: 0.7 # If I am in bed then there is a good chance the sun will be down, but in the summer mornings I may still be in bed
+      prob_given_false: 0.45 # If I am am awake then there is a reasonable chance the sun will be below the horizon - especially in winter
       to_state: "below_horizon"
     - platform: "state"
       entity_id: "sensor.android_charger_type"