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On Bounding the Union Probability Jun Yang (joint work with Fady Alajaji and Glen Takahara) Department of Statistical Sciences, University of Toronto May 23, 2015 On Bounding P ( N Jun Yang (University of Toronto) i =1 A i ) May 23, 2015


  1. On Bounding the Union Probability Jun Yang (joint work with Fady Alajaji and Glen Takahara) Department of Statistical Sciences, University of Toronto May 23, 2015 On Bounding P ( � N Jun Yang (University of Toronto) i =1 A i ) May 23, 2015 1 / 30

  2. Outline Problem Formulation 1 New Bounds using { P ( A i ) } and { � j P ( A i ∩ A j ) } 2 New Bounds using { P ( A i ) } and { � j c j P ( A i ∩ A j ) } 3 New Bounds using { P ( A i ) } and { P ( A i ∩ A j ) } 4 Summary of Main Results 5 On Bounding P ( � N Jun Yang (University of Toronto) i =1 A i ) May 23, 2015 2 / 30

  3. Problem Formulation Outline Problem Formulation 1 New Bounds using { P ( A i ) } and { � j P ( A i ∩ A j ) } 2 New Bounds using { P ( A i ) } and { � j c j P ( A i ∩ A j ) } 3 New Bounds using { P ( A i ) } and { P ( A i ∩ A j ) } 4 Summary of Main Results 5 On Bounding P ( � N Jun Yang (University of Toronto) i =1 A i ) May 23, 2015 3 / 30

  4. Problem Formulation Problem Formulation Consider a finite family of events A 1 , . . . , A N in a finite probability space (Ω , F , P ), where N is a fixed positive integer. �� N � We are interested in bounding P i =1 A i in terms of the individual event probabilities P ( A i )’s and the pairwise event probabilities P ( A i ∩ A j )’s. For example, � N � �� � � max P ( A i ) ≤ P ≤ min P ( A i ) , 1 . (1) A i i i =1 i � N � � � � ≥ P ( A i ) − P ( A i ∩ A j ) . (2) P A i i =1 i i < j On Bounding P ( � N Jun Yang (University of Toronto) i =1 A i ) May 23, 2015 4 / 30

  5. Problem Formulation Dawson-Sankoff (DS) Bound, 1967 For each outcome x ∈ F , let the degree of x , denoted by deg( x ), be the number of A i ’s that contain x . Define a ( k ) := P ( { x ∈ � i A i , deg( x ) = k } ), then one can verify �� � N � = a ( k ) , P A i i k =1 N � � P ( A i ) = ka ( k ) , (3) k =1 i N � k � � � P ( A i ∩ A j ) = a ( k ) . 2 i < j k =2 On Bounding P ( � N Jun Yang (University of Toronto) i =1 A i ) May 23, 2015 5 / 30

  6. Problem Formulation Dawson-Sankoff (DS) Bound, 1967 �� � i P ( A i ) , � Using ( θ 1 , θ 2 ) := i < j P ( A i ∩ A j ) , the Dawson-Sankoff (DS) Bound: � N � κθ 2 (1 − κ ) θ 2 � 1 1 P A i ≥ + , (4) (2 − κ ) θ 1 + 2 θ 2 (1 − κ ) θ 1 + 2 θ 2 i =1 where κ = 2 θ 2 θ 1 − ⌊ 2 θ 2 θ 1 ⌋ and ⌊ x ⌋ denotes the largest integer less than or equal to x , is the solution of the linear programming (LP) problem: N N � � min a ( k ) , ka ( k ) = θ 1 , s.t. { a ( k ) } k =1 k =1 N (5) � k � � a ( k ) = θ 2 , 2 k =2 a ( k ) ≥ 0 , k = 1 , . . . , N . On Bounding P ( � N Jun Yang (University of Toronto) i =1 A i ) May 23, 2015 6 / 30

  7. New Bounds using { P ( A i ) } and { � j P ( A i ∩ A j ) } Outline Problem Formulation 1 New Bounds using { P ( A i ) } and { � j P ( A i ∩ A j ) } 2 New Bounds using { P ( A i ) } and { � j c j P ( A i ∩ A j ) } 3 New Bounds using { P ( A i ) } and { P ( A i ∩ A j ) } 4 Summary of Main Results 5 On Bounding P ( � N Jun Yang (University of Toronto) i =1 A i ) May 23, 2015 7 / 30

  8. New Bounds using { P ( A i ) } and { � j P ( A i ∩ A j ) } Kuai-Alajaji-Takahara (KAT) Bound, 2000 Remind that a ( k ) := P ( { x ∈ � i A i , deg( x ) = k } ) Define a i ( k ) = P ( { x ∈ A i , deg( x ) = k ), one can verify �� � N a i ( k ) � � � � a i ( k ) = ka ( k ) , ⇒ P A i = a ( k ) = , k i =1 i k k i (6) N N � � � P ( A i ) = a i ( k ) , P ( A i ∩ A j ) = ( k − 1) a i ( k ) . k =1 j : j � = i k =2 We are able to use � � P ( A 1 ) , . . . , P ( A N ) , � j : j � =1 P ( A 1 ∩ A j ) , . . . , � j : j � = N P ( A N ∩ A j ) . On Bounding P ( � N Jun Yang (University of Toronto) i =1 A i ) May 23, 2015 8 / 30

  9. New Bounds using { P ( A i ) } and { � j P ( A i ∩ A j ) } Kuai-Alajaji-Takahara (KAT) Bound, 2000 γ i := � j P ( A i ∩ A j ) = P ( A i ) + � Let α i := P ( A i ) , j : j � = i P ( A i ∩ A j ). The KAT bound is the solution of the following LP problem: N N N a i ( k ) � � � min , a i ( k ) = α i , i = 1 , . . . , N , s.t. k { a i ( k ) ≥ 0 } k =1 i =1 k =1 (7) N � ka i ( k ) = γ i , i = 1 , . . . , N . k =1 which is given by � N α i − ⌊ γ i γ i � N �� � � α i ⌋ 1 � � P A i ≥ α i ⌋ − α i , (8) ⌊ γ i (1 + ⌊ γ i α i ⌋ )( ⌊ γ i α i ⌋ ) i =1 i =1 where ⌊ x ⌋ is the largest positive integer less than or equal to x . On Bounding P ( � N Jun Yang (University of Toronto) i =1 A i ) May 23, 2015 9 / 30

  10. New Bounds using { P ( A i ) } and { � j P ( A i ∩ A j ) } New Lower Bounds which are sharper than KAT Bound Recall that a ( k ) := P ( { x ∈ � i A i , deg( x ) = k } ) and a i ( k ) = P ( { x ∈ A i , deg( x ) = k ), then we observe a ( k ) ≥ a i ( k ) for all � i a i ( k ) i and all k . Also, since a ( k ) = , one can write k � i a i ( k ) ≥ a i ( k ) k for all i and all k . As a special case for k = N , it reduces to a 1 ( N ) = a 2 ( N ) = · · · = a N ( N ) . On Bounding P ( � N Jun Yang (University of Toronto) i =1 A i ) May 23, 2015 10 / 30

  11. New Bounds using { P ( A i ) } and { � j P ( A i ∩ A j ) } New Optimal Lower Bound ℓ NEW-1 The solution of the following LP problem: N N a i ( k ) � � min , k { a i ( k ) } k =1 i =1 N � s.t. a i ( k ) = P ( A i ) , i = 1 , . . . , N , k =1 (9) N � � ( k − 1) a i ( k ) = P ( A i ∩ A j ) , i = 1 , . . . , N , k =1 j : j � = i � i a i ( k ) ≥ a i ( k ) , i = 1 , . . . , N , k = 1 , . . . , N , k a i ( k ) ≥ 0 , k = 1 , . . . , N , i = 1 , . . . , N . is optimal in the class of lower bounds which are functions of � � P ( A 1 ) , . . . , P ( A N ) , � j : j � =1 P ( A 1 ∩ A j ) , . . . , � j : j � = N P ( A N ∩ A j ) . On Bounding P ( � N Jun Yang (University of Toronto) i =1 A i ) May 23, 2015 11 / 30

  12. New Bounds using { P ( A i ) } and { � j P ( A i ∩ A j ) } New Analytical Lower Bound ℓ NEW-2 The new analytical lower bound is the solution of the LP problem: N N N a i ( k ) � � � min , a i ( k ) = P ( A i ) , i = 1 , . . . , N , s.t. k { a i ( k ) ≥ 0 } i =1 k =1 k =1 N (10) � � ( k − 1) a i ( k ) = P ( A i ∩ A j ) , i = 1 , . . . , N , k =1 j : j � = i a 1 ( N ) = a 2 ( N ) = · · · = a N ( N ) . The new analytical lower bound is given by � N   γ ′ i − χ ( γ ′   � N i ) i i 1  α ′ α ′  � �  α ′ ≥ δ + −  , (11) P A i  i χ ( γ ′ [1 + χ ( γ ′ i )][ χ ( γ ′ i i ) i i i )]  i =1 i =1 α ′ α ′ α ′ where δ := { max i [ γ i − ( N − 1) α i ] } + ≥ 0 , α ′ i := α i − δ, γ ′ i := γ i − N δ , and � n − 1 if x = n where n ≥ 2 is a integer χ ( x ) := ⌊ x ⌋ otherwise On Bounding P ( � N Jun Yang (University of Toronto) i =1 A i ) May 23, 2015 12 / 30

  13. New Bounds using { P ( A i ) } and { � j c j P ( A i ∩ A j ) } Outline Problem Formulation 1 New Bounds using { P ( A i ) } and { � j P ( A i ∩ A j ) } 2 New Bounds using { P ( A i ) } and { � j c j P ( A i ∩ A j ) } 3 New Bounds using { P ( A i ) } and { P ( A i ∩ A j ) } 4 Summary of Main Results 5 On Bounding P ( � N Jun Yang (University of Toronto) i =1 A i ) May 23, 2015 13 / 30

  14. New Bounds using { P ( A i ) } and { � j c j P ( A i ∩ A j ) } Gallot-Kounias (GK) Bound, 1968 The GK bound is an analytical bound which fully uses { P ( A i ) } and { P ( A i ∩ A j ) } . Recently, it was re-visited by Feng-Li-Shen 1 that the GK bound can be obtained by � N � i c i P ( A i )] 2 [ � � ≥ ℓ GK = max k c k P ( A i ∩ A k ) . (12) P A i � � i c i c ∈ R N i − 1 If ignoring the maximization over c , the RHS is a lower bound using � i c i P ( A i ) and � k c k P ( A i ∩ A k ). 1 “Some inequalities in functional analysis, combinatorics, and probability theory”, The Electronic Journal of Combinatorics, 2010. On Bounding P ( � N Jun Yang (University of Toronto) i =1 A i ) May 23, 2015 14 / 30

  15. New Bounds using { P ( A i ) } and { � j c j P ( A i ∩ A j ) } �� N � New expressions for P i =1 A i Denote B as the collection of all non-empty subsets of { 1 , 2 , . . . , N } and let B ∈ B be a non-empty subset of { 1 , 2 , . . . , N } , p B := p ( { ω B , ω B ∈ A i for all i ∈ B , ω B / ∈ A i for all i / ∈ B } ) . (13) �� N � Then we have a new (novel) expression of P i =1 A i for any given c : � N � N � � c i p B � � � � = p B = . (14) P A i � k ∈ B c k i =1 B ∈ B i =1 B ∈ B : i ∈ B Furthermore, we have � P ( A i ) = p B , B ∈ B : i ∈ B (15) N �� � � � � � c k P ( A i ∩ A k ) = c k p B = c k p B . k =1 B : i ∈ B , k ∈ B B : i ∈ B k ∈ B k On Bounding P ( � N Jun Yang (University of Toronto) i =1 A i ) May 23, 2015 15 / 30

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