Content deleted Content added
No edit summary |
m link joint probability distribution using Find link |
||
Line 81:
===Mutual information maximizing input clustering (MIMIC)===
The MIMIC<ref>{{cite journal|last1=Bonet|first1=Jeremy S. De|last2=Isbell|first2=Charles L.|last3=Viola|first3=Paul|title=MIMIC: Finding Optima by Estimating Probability Densities|journal=Advances in Neural Information Processing Systems|date=1 January 1996|pages=424|url=http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.47.6497|publisher=The MIT Press}}</ref> factorizes the [[joint probability distribution]] in a chain-like model representing successive dependencies between variables. It finds a permutation of the decision variables, <math>r : i \mapsto j</math>, such that <math>x_{r(1)}x_{r(2)},\dots,x_{r(N)}</math> minimizes the [[Kullback-Leibler divergence]] in relation to the true probability distribution, i.e. <math>\pi_{r(i+1)} = \{X_{r(i)}\}</math>. MIMIC models a distribution
<math>
| |||