Andô–Douglas type characterization of optional projections and predictable projections
نویسندگان
چکیده
منابع مشابه
Optional and predictable projections of normal integrands and convex-valued processes
This article studies optional and predictable projections of integrands and convex-valued stochastic processes. The existence and uniqueness are shown under general conditions that are analogous to those for conditional expectations of integrands and random sets. In the convex case, duality correspondences between the projections and projections of epigraphs are given. Consistently with the gen...
متن کاملIdeal Projections and forcing Projections
It is well known that saturation of ideals is closely related to the “antichain-catching” phenomenon from Foreman-Magidor-Shelah [10]. We consider several antichain-catching properties that are weaker than saturation, and prove: (1) If I is a normal ideal on ω2 which satisfies stationary antichain catching, then there is an inner model with a Woodin cardinal; (2) For any n ∈ ω, it is consistent...
متن کاملA note on lifting projections
Suppose $pi:mathcal{A}rightarrow mathcal{B}$ is a surjective unital $ast$-homomorphism between C*-algebras $mathcal{A}$ and $mathcal{B}$, and $0leq aleq1$ with $ain mathcal{A}$. We give a sufficient condition that ensures there is a proection $pin mathcal{A}$ such that $pi left( pright) =pi left( aright) $. An easy consequence is a result of [L. G. Brown and G. k. Pedersen, C*-algebras of real...
متن کاملType Theory and Projections for Static
A system of annotated types is proposed as a means of describing and inferring static information, such as strictness and constancy, about functional programs. An abstract semantics is given in terms of projections. A close connection between annotated type assignment and projection analysis is demonstrated.
متن کاملRANDOM PROJECTIONS Margin-constrained Random Projections And Very Sparse Random Projections
Abstract We1 propose methods for improving both the accuracy and efficiency of random projections, the popular dimension reduction technique in machine learning and data mining, particularly useful for estimating pairwise distances. Let A ∈ Rn×D be our n points in D dimensions. This method multiplies A by a random matrix R ∈ RD×k, reducing the D dimensions down to just k . R typically consists ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Indagationes Mathematicae
سال: 2015
ISSN: 0019-3577
DOI: 10.1016/j.indag.2015.01.001