Our second main result, Theorem 2, applies this criterion to networks generated from a broad class of random graph ensembles with a randomly chosen binary measurement vector M. We show that the importance measures of individual edges cluster tightly around one of two values.
Third, we obtained a theoretical result showing that stochastic shielding works for an ensemble of random graphs with arbitrarily chosen binary measurement vectors, analogous to the identification of nodes as conducting versus nonconducting in ion-channel models.
3, we consider an ensemble of random graphs such as the Erdös Rényi ensemble with randomly assigned binary measurement vector M and prove our main result, which is a statement about the expected value of R k.
We have focused here on discrete state ion-channel models with binary measurement vectors.
In Sect. 5 we discuss possible extensions of our results to examples including signal transduction networks and calcium-induced calcium release models, as well as systems with graded rather than binary measurement functionals.
The proposed approach, in the exploratory setting, is illustrated using data on socioeconomic position and body mass index (BMI) from a cohort of 2,192 men and women, with binary measurements of the exposure, socioeconomic position, at ages 4, 26, and 43 years, and a continuous measurement of the outcome, BMI, at age 53 years.
In this paper, we formulate the construction of sparse binary measurement matrices from an optimization standpoint.
We also adapted a Bayesian method originally proposed by Warren et al. [ 15] for spatio-temporal data to handle longitudinal exposure measurements in association with a binary outcome of interest.
In the presence of measurement dropout, a recursive three-step information filter with missing measurement (RTSIFMM) is also developed, in which the missing measurement is modelled as Bernoulli process with a binary variable.
The primary outcome measurement for each participant was the achievement of each quality indicator with a binary (yes/no) score.
