dDTF (Korzeniewska et al., 2003), a VAR-based spectral connectivity measure, was calculated by using the Source Information Flow Toolbox (SIFT) (Delorme et al., 2011) together with other libraries, such as Granger Causal Connectivity Analysis (Seth, 2010) and Brain-System for Multivariate AutoRegressive Timeseries (Cui et al., 2008).
Posterior inference for fixed parameters and dynamic latent factors is performed via a custom tailored Markov chain Monte Carlo scheme for multivariate dynamic systems that combines extended Kalman filter-based Metropolis–Hastings proposal densities with block-sampling schemes.
This system of multivariate variational inequalities has a solution.
In this paper, we present a hybrid parallel algorithm for solving systems of multivariate constraints by exploiting both the CPU and the GPU multicore architectures.
In this article, we propose an optimization based formulation for design of optimal inputs for multivariate systems.
For multivariate systems, we derive mathematical relationships regression models, for example—from statistically designed experiments to gauge how factors (independent variables) affect response variables (dependent variables): how does the global temperature or the sea level (response variables) change with the global CO2 concentration (factor).
To date, two general systems of multivariate regressions have been proposed to account for dependencies between pairs of species in a regional species pool.
As the Marshall Classification holds comparable prognostic value to age, GCS, pupillary reactivity, SAH etc. [ 4, 7] and trauma registries commonly do not have record of this classification, the translation of AIS codes to the Marshall System opens up the possibility for multivariate prognostic analysis of large series of TBI subjects saved in trauma registries.
Fast Monte Carlo methodology for multivariate particulate systems-I: point ensemble Monte Carlo.
