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Limit Laws for the Number of Groups formed by Social Animals under the Extra Clustering Model (joint with Michael Drmota and Yi-Wen Lee) Michael Fuchs Institute of Applied Mathematics National Chiao Tung University June 19th, 2014 Michael


  1. Limit Laws for the Number of Groups formed by Social Animals under the Extra Clustering Model (joint with Michael Drmota and Yi-Wen Lee) Michael Fuchs Institute of Applied Mathematics National Chiao Tung University June 19th, 2014 Michael Fuchs (NCTU) Animal Group Patterns June 19th, 2014 1 / 30

  2. Probabilistic Analysis of a Genealogical Model of Animal Group Patterns Michael Fuchs (NCTU) Animal Group Patterns June 19th, 2014 2 / 30

  3. Phylogenetic Tree Ordered, binary, rooted tree with leafs representing the animals. Describes the genetic relatedness of animals. Michael Fuchs (NCTU) Animal Group Patterns June 19th, 2014 3 / 30

  4. Yule-Harding Model (Bottom-Up) Fundamental random model in phylogenetics. Michael Fuchs (NCTU) Animal Group Patterns June 19th, 2014 4 / 30

  5. Yule-Harding Model (Bottom-Up) Fundamental random model in phylogenetics. Uniformly choose a pair of yellow nodes and let them coalesce. Michael Fuchs (NCTU) Animal Group Patterns June 19th, 2014 4 / 30

  6. Yule-Harding Model (Bottom-Up) Fundamental random model in phylogenetics. Uniformly choose a pair of yellow nodes and let them coalesce. Michael Fuchs (NCTU) Animal Group Patterns June 19th, 2014 4 / 30

  7. Yule-Harding Model (Bottom-Up) Fundamental random model in phylogenetics. Uniformly choose a pair of yellow nodes and let them coalesce. Michael Fuchs (NCTU) Animal Group Patterns June 19th, 2014 4 / 30

  8. Yule-Harding Model (Bottom-Up) Fundamental random model in phylogenetics. Uniformly choose a pair of yellow nodes and let them coalesce. Michael Fuchs (NCTU) Animal Group Patterns June 19th, 2014 4 / 30

  9. Yule-Harding Model (Bottom-Up) Fundamental random model in phylogenetics. Uniformly choose a pair of yellow nodes and let them coalesce. Michael Fuchs (NCTU) Animal Group Patterns June 19th, 2014 4 / 30

  10. Yule-Harding Model (Bottom-Up) Fundamental random model in phylogenetics. Uniformly choose a pair of yellow nodes and let them coalesce. Michael Fuchs (NCTU) Animal Group Patterns June 19th, 2014 4 / 30

  11. Animal Groups under the Yule-Harding Model Durand, Blum and Fran¸ cois (2007) : Groups are formed more likely by animals which are genetically related. Michael Fuchs (NCTU) Animal Group Patterns June 19th, 2014 5 / 30

  12. Animal Groups under the Yule-Harding Model Durand, Blum and Fran¸ cois (2007) : Groups are formed more likely by animals which are genetically related. − → neutral model. Michael Fuchs (NCTU) Animal Group Patterns June 19th, 2014 5 / 30

  13. Animal Groups under the Yule-Harding Model Durand, Blum and Fran¸ cois (2007) : Groups are formed more likely by animals which are genetically related. − → neutral model. Clade of a leaf: All leafs of the tree rooted at the parent. Michael Fuchs (NCTU) Animal Group Patterns June 19th, 2014 5 / 30

  14. Animal Groups under the Yule-Harding Model Durand, Blum and Fran¸ cois (2007) : Groups are formed more likely by animals which are genetically related. − → neutral model. Clade of a leaf: All leafs of the tree rooted at the parent. Michael Fuchs (NCTU) Animal Group Patterns June 19th, 2014 5 / 30

  15. Animal Groups under the Yule-Harding Model Durand, Blum and Fran¸ cois (2007) : Groups are formed more likely by animals which are genetically related. − → neutral model. # of groups � # of maximal clades Michael Fuchs (NCTU) Animal Group Patterns June 19th, 2014 6 / 30

  16. Animal Groups under the Yule-Harding Model Durand, Blum and Fran¸ cois (2007) : Groups are formed more likely by animals which are genetically related. − → neutral model. # of groups � # of maximal clades � 2 Michael Fuchs (NCTU) Animal Group Patterns June 19th, 2014 6 / 30

  17. Yule-Harding Model (Top-Down) Alternative description of Yule-Harding model: Michael Fuchs (NCTU) Animal Group Patterns June 19th, 2014 7 / 30

  18. Yule-Harding Model (Top-Down) Alternative description of Yule-Harding model: Uniformly choose a yellow node and replace it by a cheery. Michael Fuchs (NCTU) Animal Group Patterns June 19th, 2014 7 / 30

  19. Yule-Harding Model (Top-Down) Alternative description of Yule-Harding model: Uniformly choose a yellow node and replace it by a cheery. Michael Fuchs (NCTU) Animal Group Patterns June 19th, 2014 7 / 30

  20. Yule-Harding Model (Top-Down) Alternative description of Yule-Harding model: Uniformly choose a yellow node and replace it by a cheery. Michael Fuchs (NCTU) Animal Group Patterns June 19th, 2014 7 / 30

  21. Yule-Harding Model (Top-Down) Alternative description of Yule-Harding model: Uniformly choose a yellow node and replace it by a cheery. Michael Fuchs (NCTU) Animal Group Patterns June 19th, 2014 7 / 30

  22. # of Groups X n = # of groups under the Yule Harding model Michael Fuchs (NCTU) Animal Group Patterns June 19th, 2014 8 / 30

  23. # of Groups X n = # of groups under the Yule Harding model We have, � 1 , if I n = 1 or I n = n − 1 , d X n = X I n + X ∗ n − I n , otherwise, where I n = Uniform { 1 , . . . , n − 1 } is the # of animals in the left subtree and X ∗ n is an independent copy of X n . Michael Fuchs (NCTU) Animal Group Patterns June 19th, 2014 8 / 30

  24. # of Groups X n = # of groups under the Yule Harding model We have, � 1 , if I n = 1 or I n = n − 1 , d X n = X I n + X ∗ n − I n , otherwise, where I n = Uniform { 1 , . . . , n − 1 } is the # of animals in the left subtree and X ∗ n is an independent copy of X n . Theorem (Durand and Fran¸ cois; 2010) We have, a := 1 − e − 2 � � E ( X n ) ∼ an . 4 Michael Fuchs (NCTU) Animal Group Patterns June 19th, 2014 8 / 30

  25. Comparison with Real-life Data Durand, Blum and Fran¸ cois (2007) presented the following data: Michael Fuchs (NCTU) Animal Group Patterns June 19th, 2014 9 / 30

  26. Extra Clustering Model Durand, Blum and Fran¸ cois (2007): Let p ≥ 0 . We have, � 1 , with probability p d X n = neutral model , otherwise . Michael Fuchs (NCTU) Animal Group Patterns June 19th, 2014 10 / 30

  27. Extra Clustering Model Durand, Blum and Fran¸ cois (2007): Let p ≥ 0 . We have, � 1 , with probability p d X n = neutral model , otherwise . Remark: p = 0 is neutral model. Michael Fuchs (NCTU) Animal Group Patterns June 19th, 2014 10 / 30

  28. Extra Clustering Model Durand, Blum and Fran¸ cois (2007): Let p ≥ 0 . We have, � 1 , with probability p d X n = neutral model , otherwise . Remark: p = 0 is neutral model. Introduced to test whether or not genetic relatedness is the sole driving force behind the group formation process. Michael Fuchs (NCTU) Animal Group Patterns June 19th, 2014 10 / 30

  29. Average Number of Groups Theorem (Durand and Fran¸ cois; 2010) We have,  c ( p ) Γ(2(1 − p )) n 1 − 2 p , if p < 1 / 2;       log n   , if p = 1 / 2; E ( X n ) ∼ 2  p   2 p − 1 , if p > 1 / 2 ,      where � 1 1 (1 − t ) − 2 p e 2(1 − p ) t � 1 − (1 − p ) t 2 � c ( p ) := d t. e 2(1 − p ) 0 Michael Fuchs (NCTU) Animal Group Patterns June 19th, 2014 11 / 30

  30. Testing for the Neutral Model Durand, Blum and Fran¸ cois (2007): Michael Fuchs (NCTU) Animal Group Patterns June 19th, 2014 12 / 30

  31. Yi-Wen’s Thesis (2012) Michael Fuchs (NCTU) Animal Group Patterns June 19th, 2014 13 / 30

  32. Variance and SLLN Theorem (Lee; 2012) We have, Var( X n ) ∼ (1 − e − 2 ) 2 n log n = 4 a 2 n log n. 4 Michael Fuchs (NCTU) Animal Group Patterns June 19th, 2014 14 / 30

  33. Variance and SLLN Theorem (Lee; 2012) We have, Var( X n ) ∼ (1 − e − 2 ) 2 n log n = 4 a 2 n log n. 4 Theorem (Lee; 2012) We have, � � � � X n � � P lim E ( X n ) − 1 � = 0 = 1 . � � n →∞ � For SLLN, X n is constructed on the same probability space via the tree evolution process underlying the Yule-Harding model. Michael Fuchs (NCTU) Animal Group Patterns June 19th, 2014 14 / 30

  34. Higher Moments Theorem (Lee; 2012) For all k ≥ 3 , E ( X n − E ( X n )) k ∼ ( − 1) k 2 k k − 2 a k n k − 1 . Michael Fuchs (NCTU) Animal Group Patterns June 19th, 2014 15 / 30

  35. Higher Moments Theorem (Lee; 2012) For all k ≥ 3 , E ( X n − E ( X n )) k ∼ ( − 1) k 2 k k − 2 a k n k − 1 . This implies that all moments larger than two of X n − E ( X n ) � Var( X n ) tend to infinity! Michael Fuchs (NCTU) Animal Group Patterns June 19th, 2014 15 / 30

  36. Higher Moments Theorem (Lee; 2012) For all k ≥ 3 , E ( X n − E ( X n )) k ∼ ( − 1) k 2 k k − 2 a k n k − 1 . This implies that all moments larger than two of X n − E ( X n ) � Var( X n ) tend to infinity! Question: Is there a limit distribution? Michael Fuchs (NCTU) Animal Group Patterns June 19th, 2014 15 / 30

  37. Random Recursive Trees Unordered, rooted trees. Michael Fuchs (NCTU) Animal Group Patterns June 19th, 2014 16 / 30

  38. Random Recursive Trees Unordered, rooted trees. Uniformly choose one of the nodes and attach a child. Michael Fuchs (NCTU) Animal Group Patterns June 19th, 2014 16 / 30

  39. Random Recursive Trees Unordered, rooted trees. Uniformly choose one of the nodes and attach a child. Michael Fuchs (NCTU) Animal Group Patterns June 19th, 2014 16 / 30

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