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In this article, we introduce the multivariate slash and skew-slash t distributions which provide alternative choices in simulating and fitting skewed and heavy tailed data.
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The SP Platform (Fig. 1) is a web-based tool aimed at observing travellers' route choices in simulated Intelligent Transportation Systems ITSS) contexts.
Since each cell in the signalling model contains a core oscillator mechanism, we retain the non-dimensional parameter choices made in simulating the core oscillator model, which allows us to retain the oscillatory dynamics discussed in section 2.2.1.
The first part of this study is motivated by the necessity to evaluate and compare various transport models and to justify their choice and use in simulating the Gunn diodes in DC and transient regimes.
The results demonstrate how the residential location choice model works with other inter-connected models, such as work and non-work location choices, dwelling tenure and dwelling type, and vehicle fleet size choice embedded in the modelling system, in simulating the impact of transport and housing development on household choices of residential location.
Since, an appropriate choice of primary variables is critical in simulating multiphase subsurface flow, depending on such a choice, the governing equations can be expressed in different forms.
This paper is focused on calibration of an intelligent network simulation model (INSIM) with real-life transportation network to analyse the INSIM's feasibility in simulating commuters' travel choice behaviour under the influence of real-time integrated multimodal traveller information (IMTI).
As an example of balancing these competing criteria, consider my choice of a 100-monomer (at 2.45 nm per monomer) rigid segment size in simulating ParM mediated segregation of plasmids in E. coli with longest axis of 3 µm.
In simulating resistance, it enables users to simulate rape.
The data partitioning choice in the simulated data sets is made to emulate the choices made in the experimental data set.
Regulating fitness to drive is best achieved by combining information regarding real-world patient choices and observation of driving in simulated or real-world situations, and relating them to measures of VF loss [ 33].
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