Talk:Marginal distribution

message: I find the diagram a little hard to relate to the text at the moment. I think it would be very helpful indeed if someone could add a small description of what the different coloured regions represent, for example.Jimjamjak (talk) 17:06, 16 December 2011 (UTC)
thread_title: Diagram

message: I guess that most readers are (like me) beginners at probability and statistics. It would be very good if someone could ad an easy pedagogical explanation as a supplement to the formula. Thank you! Aaker (talk) 18:48, 14 December 2008 (UTC)
replies: text: Yes, the explanation should be simpler.  Their explanation basically took some very simple rudimentary statistics, and put it in terms that would be way to complex for anyone who didn't already know what Marginal distribution was.--Kodiak42 (talk) 01:06, 29 August 2012 (UTC)
thread_title: Simple explanation 

message: Surely it should be interpreted as a 28.5% chance of getting hit if you *ignore* the traffic light rather than walk across the road blindfold?!
:yes, otherwise the new conditions alter the original probability distribution. I've updated the example.  —Preceding unsigned comment added by 131.111.184.8 (talk) 20:09, 16 May 2010 (UTC)
thread_title:  Incorrect Interpretation? 

message: Thanks for the "Real World Example". However, the values for the conditional probabilites sum up to one:
P(H=hit|L=red) = 0.01, P(H=hit|L=yellow) = 0.09, P(H=hit|L=green) = 0.9
Although this might be the case, in general I think i won't (e.g. assume very alert drivers, when one is only hit with probability of 0.5 although the lights are green; or Saturday night rushour, when drunk drivers hit any pedestrian crossing). So, for the sake of clarity, should the values in the example be modified? Please correct me if I am wrong.
Melwin Quacke (talk) 12:20, 24 November 2009 (UTC)
replies: text: You're correct, only the columns of the conditional probability table should add up to 1. updated.  —Preceding unsigned comment added by 131.111.184.8 (talk) 21:03, 16 May 2010 (UTC)
thread_title:  Unfavourable Example values? 

message: Thumbs up for the "Real-World Example".  I wish every theoretical WP article had such clear examples.  Beginners can very easily get lost in jargon.  The example really helps readability.  —Preceding unsigned comment added by 207.22.49.29 (talk) 00:40, 11 January 2010 (UTC)
replies: text: I aggree the real world example is really good and a big help. I think it can be interpreted exactly as 130.154.0.250 said. I certainly do not see any reason why it shouldn't be true. Basically one has the conditional probabilities of what happens when each light is on, plus the probability of each light being on at any given time, and then we do not know when the person, who ignores the light, crosses. Therefore "the probability of a pedestrian being hit if they don't pay attention to the condition of the traffic light is 0.572". I believe this conclusion can be added to the article in that way. Regards, Kmw2700 (talk) 12:07, 29 September 2015 (UTC)
thread_title:  Real-World Example 

message: There is something I don't understand in this explanation: why are the joint distribution and conditional distribution equal here? Nova77 05:14, 11 Feb 2005 (UTC)
replies: text: They're not.  I've looked at old versions and I can't find what makes you think they are. Michael Hardy 19:57, 7 May 2005 (UTC)
thread_title: joint distribution and conditional distribution equal? 

message: Very nicely explained article, especially as it includes an actual worked example. Regarding this example, I have a comment.
In the example, it says:
"So in this case the answer for the marginal probability can be found by summing P(H,L) for all possible values of L, with each value of L weighted by its probability of occurring."
But shouldn't it be "P(H | L)", i.e. the conditional probability distribution? At least in the formula for P_X(x) only the conditional probability distribution is weighted by the probability of P_Y(y).
The joint probability distributions is simply summarized, without any weights.
Thank you very much for clarifying this.
158.64.77.254 (talk) 07:07, 18 September 2014 (UTC)
thread_title:  In the example, shouldn't it say that the _conditional_ probability distribution is being weighted by <math>P_y(y)</math> instead of the _joint_ probability distribution? 

message: When we say "someone was crossing the road on red light" we mean the light that was flashed for the person crossing the street and not for the traffic.  — Preceding unsigned comment added by 109.252.73.65 (talk) 00:21, 30 March 2017 (UTC)
thread_title: Real world example doesn't make sense 

message: I have a Markov network is:
X_1 is connected to X_2 and X_3, X_2 is connected to X_4, X_3 is connected to X_4
Given the marginal distribution: P(X_2, X_3) = \sum_{X_1, X_4} (X_1, X_2, X_3, X_4), what is the set of marginal independent statements? Inlin (talk) 20:42, 2 September 2023 (UTC)
thread_title:  How to use a marginal distribution to determine marginal independence?