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The second focus is on the manner of involving stakeholders in the EMS process, which has stakeholder participation as one of its core principles.
The data collected will broadly be characterised as system characteristics, EMS dispatch characteristics, patient characteristics and EMS process variables.
We anticipate using descriptive statistics to summarise patient characteristics, clinical variables, EMS dispatch characteristics, EMS process variables, location and cardiac arrest event rate.
The data collected will be characterised broadly as system characteristics, emergency medical services (EMS) dispatch characteristics, patient characteristics and EMS process variables.
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Moreover, a bidirectional expectation maximization (EM) process is introduced to update both point sets in contrast to traditional methods.
We summarize the parameter estimation algorithm in Algorithm 2. This is a standard EM process, and the lower bound (mathcal {L} eta,phi,zeta ;alpha,beta,mu _{1 C},sigma _{1 C})) is a concave function.
Missing data were dealt with using the Expectation Maximisation (EM) process of the SPSS missing values procedure.
The likelihood is guaranteed to increase after each iteration in this EM process and will converge to a maximum (e.g., Lange 2002); the estimated parameter values are MLEs if this maximum is the global maximum.
In the M step, we find new parameter estimation Θ(t +1) by maximizing Equation (7) (8) It is relatively straightforward to bound the variation parameters (σ, δ+1, δ−1) during the iterative EM process to converge to a solution with 'reasonable' parameters.
This chapter is dedicated to the simulation of electromagnetic (EM) processes in tokamaks.
Two researchers (AF and EM) processed and compared the data from the interviews, email questionnaire, and direct observations.
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