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"Constructive Models of Discrete and Continuous Physical Phenomena".
More specifically, we apply these techniques to linear time-invariant dynamic systems and Petri net models of discrete event systems.
Recently, Galton developed a theory of discrete space that parallels RCC, but question still lies in that can we have a theory of qualitative spatial reasoning admitting models of discrete spaces as well as continuous spaces?
Hotz, V. Joseph, et al. "A simulation estimator for dynamic models of discrete choice". The Review of Economic Studies 61.2 (1994): 265-289.
The original Oaxaca-Blinder method of linearly decomposing inter-group differences in means into an 'explained' and 'residual' component has been extended to decomposing inter-group differences in probabilities, derived from models of discrete choice (Nielsen 1998).
Although a commendable number of fast estimation algorithms are available for Bayesian models of continuous Gaussian traits, there is a shortage for corresponding models of discrete or censored phenotypes.
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In combination with its support for extended finite-state automata (EFA), these features enable Supremica users to develop highly complex models of discrete-event systems using a wide variety of modelling styles.
A brief discussion of safe diagnosability for timed models of discrete-event systems is also provided.
The basic modality remains: data-driven models of Discrete-space- and time-walkers, mined from available sources.
"We have kind of broken free of the model of discrete articles that have a beginning and end.
The Hidden Markov Model (HMM) is one of the mainstays of statistical modeling of discrete time series, with applications including speech recognition, computational biology, computer vision and econometrics.
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