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Research to date has focused on identifying effective transition models and creating recommendations for successful transitional care.
State transition models have been extended to accommodate certain numerical quantities, such as time stamps and probabilities.
In terms of visual modelling we evaluate non-gesture-models, length modelling and universal transition models.
The transition models can be calculated by solving the optimization problems.
This study is concerned with the complexity of state transition models (STM), and synchronization.
Requirements for engineering transition models suitable for industrial CFD codes are specified.
In specific domains (e.g., interview videos), high-level transition models are also explored to model the probabilistic transition patterns among speakers.
We introduce a framework for model learning and planning in stochastic domains with continuous state and action spaces and non-Gaussian transition models.
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Prior modeling work in asthma has been limited to Markov state-transition models.
Ecological responses were represented as state-and-transition models (STMs), and analyzed as mathematical graphs.
State-transition models are employed to project future prevalence rates of risk factors and diseases within populations.
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