Probability operator and statistical averages

Author(s):  
Phil Attard
Keyword(s):  
Author(s):  
E. E. Entralgo ◽  
V. V. Kuryshkin ◽  
Yu. I. Zaparovanny
Keyword(s):  

1980 ◽  
Vol 35 (4) ◽  
pp. 437-441 ◽  
Author(s):  
W. Rehder

Abstract Necessary and sufficient conditions for commutativity of two projections in Hilbert space are given through properties of so-called conditional connectives which are derived from the conditional probability operator PQP. This approach unifies most of the known proofs, provides a few new criteria, and permits certain suggestive interpretations for compound properties of quantum-mechanical systems.


Author(s):  
DARIUSZ CHRUŚCIŃSKI ◽  
ANDRZEJ KOSSAKOWSKI ◽  
TAKASHI MATSUOKA ◽  
MASANORI OHYA

2020 ◽  
Author(s):  
Nino Guallart

Abstract In this work we examine some of the possibilities of combining a simple probability operator with other modal operators, in particular with a belief operator. We will examine the semantics of two possible situations for expressing probabilistic belief or the lack of it, a simple subjective probability operator (SPO) versus the composition of a belief operator, plus an objective modal operator (BOP). We will study their interpretations in two probabilistic semantics: a relational Kripkean one and a variation of neighbourhood semantics, showing that the latter is able to represent the lack of probabilistic belief more directly, just with the SPO, whereas relational semantics needs the combination of BOP probability to represent lack of belief.


2017 ◽  
Vol 15 (04) ◽  
pp. 1750029 ◽  
Author(s):  
Nicola Dalla Pozza ◽  
Matteo G. A. Paris

We revisit the problem of finding the Naimark extension of a probability operator-valued measure (POVM), i.e. its implementation as a projective measurement in a larger Hilbert space. In particular, we suggest an iterative method to build the projective measurement from the sole requirements of orthogonality and positivity. Our method improves existing ones, as it may be employed also to extend POVMs containing elements with rank larger than one. It is also more effective in terms of computational steps.


1982 ◽  
Vol 25 (10) ◽  
pp. 956-960 ◽  
Author(s):  
Sudzhit Basu

Author(s):  
Qinghua Gu ◽  
Qian Wang ◽  
Neal N. Xiong ◽  
Song Jiang ◽  
Lu Chen

AbstractSurrogate-assisted optimization has attracted much attention due to its superiority in solving expensive optimization problems. However, relatively little work has been dedicated to addressing expensive constrained multi-objective discrete optimization problems although there are many such problems in the real world. Hence, a surrogate-assisted evolutionary algorithm is proposed in this paper for this kind of problem. Specifically, random forest models are embedded in the framework of the evolutionary algorithm as surrogates to improve approximate accuracy for discrete optimization problems. To enhance the optimization efficiency, an improved stochastic ranking strategy based on the fitness mechanism and adaptive probability operator is presented, which also takes into account both convergence and diversity to advance the quality of candidate solutions. To validate the proposed algorithm, it is comprehensively compared with several well-known optimization algorithms on several benchmark problems. Numerical experiments are demonstrated that the proposed algorithm is very promising for the expensive constrained multi-objective discrete optimization problems.


2007 ◽  
Vol 16 (2) ◽  
pp. 105-120 ◽  
Author(s):  
Z. Ognjanovic ◽  
A. Perovic ◽  
M. Raskovic

Sign in / Sign up

Export Citation Format

Share Document