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More than a monolithic software application, we need a modeling framework where model developers can implement their models using heterogeneous methods.
We propose a co-occurrence modeling framework where we exploit all these patterns jointly in a common semantic space, combining both supervised and unsupervised learning.
This modeling framework where the underlying dynamics of the target evolves based on a Markov chain belongs to the class of so called jump Markov nonlinear systems in the literature (see [7] and the references therein).
A parsimonious parking model is integrated into the MFD-based multimodal modeling framework, where the dynamics of vehicular and passenger flows are considered with a change in the aggregated behavior (e.g. mode choice and parking facility choice) caused by cruising and congestion.
We refer the approaches of estimating z gm using either the log ratio of means (m = 2) or the standardized mean difference (m = 1) as WROM and WSMD, respectively, in the quality-adjusted modeling framework, and as UWROM and UWSMD, respectively, in the quality-unadjusted modeling framework, where q ig = 1.
Instead, we propose a fundamentally different modeling framework where the system is seen as an oscillatory medium.
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The goal of the current modeling effort is to provide a framework where many of these models could be employed or integrated, or at the very least their hypotheses tested in a new context, specifically related to biological observations and experimental data.
Moreover, it also allows us to extend the applicability of our modeling framework to other infectious diseases where this period may last several days, therefore being non-negligible, as in the case of the severe acute respiratory syndrome (SARS) [ 34].
The analytical methods developed are set in a Bayesian framework where multistage models are fitted jointly, properly incorporating parameter estimation uncertainty at all levels of the modeling process.
In theory, however, the same modeling framework could be adapted to a scenario where we consider multiple targets with known (fixed) aspect.
The modeling framework is based on a common innovative solver, where all processes are solved using tailored numerical techniques suited to account for characteristic time scales of individual domains.
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