~x y; {o}. In the remainder of this paper, we will specialise to the case where v = o and

where the superscript n of M n denotes the number of involved nested expectations, and, for n ≥ 0, we abbreviate ˜ C b n+1 b n = ˜ C n , where we use the convention that b = v , which is the initial vertex in M N +1 v , ~x J ; C . Let A N v , ~x J ; C = M N +1 v , ~x J ; C 1, B N v , y; C = X b= · ,y M N +1 v ,b; C 1 p b , 3.20 which are both nonnegative and agree with 3.16–3.17 when N = 0. We note that A N v , ~x J ; C = B N v , y; C = 0 for N ǫ min j ∈J t x j − t v , since, by the recursive definition 3.19, the operation M N +1 eats up at least N time-units where one time-unit is ǫ. We now resume the expansion of the right-hand side of 3.18. As we notice, we have Pv C −→ ~x J again in the right-hand side of 3.18, but now with v and C being replaced by b and ˜ C b v , respectively. Applying 3.18 to its own right-hand side, we obtain P v C −→ ~x J = A v , ~x J ; C − A 1 v , ~x J ; C + X y B v , y; C − B 1 v , y; C τ~x J − y + X b 2 p b 2 M 2 v ,b 2 ; C P b 2 ˜ C1 − → ~x J . 3.21 Define Av , ~x J ; C = ∞ X N =0 −1 N A N v , ~x J ; C, Bv , y; C = ∞ X N =0 −1 N B N v , y; C. 3.22 By repeated application of 3.18 to 3.21 until the remainder vanishes which happens after a finite number of iterations, see below 3.20, we arrive at the following conclusion, which is the linear expansion for the generalised r-point function: Proposition 3.6 Linear expansion. For any J 6= ∅, λ ≤ λ c and ~x J ∈ Λ |J| , P v C −→ ~x J = Av , ~x J ; C + X y Bv , y ; C τ~x J − y. 3.23 Applying Proposition 3.6 to the r-point function in 3.3, we arrive at τ~x J = A ~x J + X y By τ~x J − y, 3.24 where we abbreviate A ~x J = Ao, ~x J ; {o}, By = Bo, y; {o}, 3.25 and similarly for A N ~x J = A N

o, ~x

J ; {o} and B N y = B N

o, y; {o}. In the remainder of this paper, we will specialise to the case where v = o and

C = {o}, and abbreviate M N ~x J X = M N o , ~x J ; {o} X N ≥ 1. 3.26 828 P v C −→ ~x J =             C v − C v 1 b + · · ·             + X y             C v y − C y v 1 b + · · ·             Figure 5: A schematic representation of the expansion 3.23. The vertices at the top of each diagram are the components of ~x J , as in Figure 3. In the second parentheses, the connection from y to ~x J in each diagram depicted in bold dashed lines represents τ~x J − y. This completes the proof of 2.12. In the next section, we will use Proposition 3.6 for a general set C in order to obtain the expansion for A ~x J . For future reference, we state a convenient recursion formula for M N +N ′ v , ~x J ; C X , valid for N , N ′ ≥ 1: M N +N ′ v , ~x J ; C X = X b N p b N M N v ,b N ; C M N ′ b N , ~x J ;˜ C bN b N −1 X , 3.27 which follows immediately from the second representation in 3.19. 4 Expansion for A ~x J We now consider A ~x J in 3.24. Our goal is to extract two factors τ~x J \I − y 1 and τ~x I − y 2 from A ~x J , for some I J with I 6= ∅ and some y 1 , y 2 ∈ Λ. Let r 1 = |J \ I| + 1 and r 2 = |I| + 1. We devote Section 4.1 to the extraction of the first r 1 -point function τ~x J \I − y 1 , and Section 4.2 to the extraction of the second r 2 -point function τ~x I − y 2 .

4.1 First cutting bond and decomposition of A

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