This important work by Veneditto and colleagues developed a new modeling approach, called a mixture-of-agent hidden Markov ... state also has a higher MBr weight than the initial state and is most ...

In this work, we introduce the transition-based reweighting analysis ... discrete TRAM, and Markov state models are special cases and can be derived from the TRAM equations. TRAM is demonstrated by ...

Their care is in a continual state of transition, whether that is moving on to new lines of therapy or taking their care services home with them, and it is up to the care team to make sure the patient ...

Typically models are built using biophysically-based mechanistic principles such as Hodgkin-Huxley or Markov ... NN-f model (Equation 8), and the NN-d model (Equation 9) to the synthetic data, where ...

Transition networks (TNs) provide an elegant method to identify (meta)stable states and the transitions between them from MD simulations. Here, we apply two different methods to generate TNs for ...

Therefore, a parameter estimation method based on extending the Markov state-space with variable transition rates for the stress-strength ... the derivation process of the relevant equations is ...