English

Boundary Disintegration for Weighted Residual Energy Trees

Probability 2026-01-22 v1 Functional Analysis

Abstract

We study iterated weighted residual (WR) splittings generated by a positive operator R0B(H)+R_{0}\in B\left(H\right)_{+} and a finite family of contractions C1,,CmC_{1},\dots,C_{m} in B(H)B\left(H\right). The associated residual update RR1/2(ICjCj)R1/2R\mapsto R^{1/2}(I-C^{*}_{j}C_{j})R^{1/2} produces an mm-ary energy tree of residuals {Rw}\left\{ R_{w}\right\} and dissipated pieces {Dw,j}\left\{ D_{w,j}\right\} indexed by finite words. From this tree we construct intrinsic path measures on the path space by biasing transitions either by a fixed quadratic form xx,Dw,jxx\mapsto\left\langle x,D_{w,j}x\right\rangle (defining the measures νx\nu_{x}) or, in the trace-class setting, by tr(Dw,j){\rm tr}\left(D_{w,j}\right) (yielding a reference measure νtr\nu_{\mathrm{tr}}). When R0S1(H)+R_{0}\in S_{1}\left(H\right)_{+}, we show that νtr\nu_{\mathrm{tr}} dominates the family {νx}\left\{ \nu_{x}\right\} and identify dνx/dνtrd\nu_{x}/d\nu_{\mathrm{tr}} as a canonical martingale limit of cylinder likelihood ratios. Along νtr\nu_{\mathrm{tr}}-almost every branch the residuals decrease to a terminal trace-class random variable RR_{\infty}, which we interpret as the WR boundary variable. We then disintegrate νtr\nu_{\mathrm{tr}} over σ(R)\sigma\left(R_{\infty}\right), obtaining a boundary law μtr=(R)#νtr\mu_{\mathrm{tr}}=\left(R_{\infty}\right)_{\#}\nu_{\mathrm{tr}} and conditional path measures {νtrT}\left\{ \nu^{T}_{\mathrm{tr}}\right\} . Finally, we show that each νx\nu_{x} admits a boundary representation as a mixture of {νtrT}\left\{ \nu^{T}_{\mathrm{tr}}\right\} with an explicit boundary density hx=dμx/dμtrh_{x}=d\mu_{x}/d\mu_{\mathrm{tr}}, thereby organizing the family of intrinsic WR path measures by a single trace-biased boundary disintegration.

Keywords

Cite

@article{arxiv.2601.14646,
  title  = {Boundary Disintegration for Weighted Residual Energy Trees},
  author = {James Tian},
  journal= {arXiv preprint arXiv:2601.14646},
  year   = {2026}
}
R2 v1 2026-07-01T09:13:31.887Z