Conditional Dependence Relationship

From GM-RKB
(Redirected from Conditional Dependence)
Jump to: navigation, search

A Conditional Dependence Relationship [math]CI(A,B,C)[/math] is a Statistical Relationship that is true if and only if ...



References

2015

  • (Wikipedia, 2015) ⇒ http://en.wikipedia.org/wiki/conditional_dependence Retrieved:2015-2-25.
    • In probability theory, conditional dependence is a relationship between two or more events that are dependent when a third event occurs.[1] [2] For example, if A and B are two events that individually affect the happening of a third event C, and do not directly affect each other, then initially (when the event C has not occurred) : [math] P(A\mid B) = P(A) \text{ or } P(B\mid A) = P(B) [/math] [3] [4] (A and B are independent) Eventually the event C occurs, and now if event A occurs the probability of occurrence of the event B will decrease (similarly event B occurring first will decrease the probability of occurrence of A in future). Hence, now the two events A and B become conditionally dependent because their probability of occurrence is dependent on either event's occurrence. Intuitively we can say that since A and B both were probable causes of C, given C has occurred, occurrence of either of A or B alone could explain away the happening of C. : [math] P(B\mid C) \gt P(B\mid C,A) [/math] [5] In essence probability comes from a person's information content about occurrence of an event. For example, let the event A be 'I have a new car'; event B be 'I have a new watch'; and event C be 'I am happy'. Let us assume that the event C has occurred – meaning 'I am happy'. Now if a third person sees my new watch, he/she will attribute this reason to my happiness. Thus in his/her view the probability of the event A ('I have a new car') to have been the cause of the event C ('I am happy') will decrease as the event C has been explained away by the event B. Conditional dependence is different from conditional independence. In conditional independence two events which are initially dependent become independent given the occurrence of a third event. [6]
  1. Introduction to Artificial Intelligence by Sebastian Thrun and Peter Norvig, 2011 "Unit 3: Conditional Dependence"
  2. Introduction to learning Bayesian Networks from Data by Dirk Husmeier [1] "Introduction to Learning Bayesian Networks from Data -Dirk Husmeier"
  3. Conditional Independence in Statistical theory "Conditional Independence in Statistical Theory", A. P. Dawid"
  4. Probabilistic independence on Britannica "Probability->Applications of conditional probability->independence (equation 7) "
  5. Introduction to Artificial Intelligence by Sebastian Thrun and Peter Norvig, 2011 "Unit 3: Explaining Away"
  6. Conditional Independence in Statistical theory "Conditional Independence in Statistical Theory", A. P. Dawid