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The pre-tightening force is determined by solving the conditional extremum of the Lagrange function.
As discussed in [13], Bayesian estimation finds the estimate of x by solving the conditional expectation hat{mathbf{x}} = mathsf{E}~ left[mathbf{x}|mathbf{y} right] = sumlimits_{mathcal{S}} p (mathcal{S}|mathbf{y}) mathsf{E} left[mathbf{x}|mathbf{y},mathcal{S} right] (4).
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The conditional reliability function, the conditional probability density and moments of first-passage time are obtained by solving the backward Kolmogorov equation and generalized Pontryagin equations with suitable initial and boundary conditions.
While this issue could be solved by the conditional disruption of all the dynamins in neurons, the generation of dynamin triple KO synapses has so far proven unfeasible due to the very long half-life of dynamin in axon endings and to the neuronal death that occurs before nerve endings are completely depleted of dynamin 2 (our unpublished observations).
* 95% CIs for conditional probabilities were estimated by sampling from beta distributions for sensitivity of MRI and mammography combined and sensitivity for each modality alone, and solving for the conditional probability.
This system can be solved by solving first the field Eq. (20).
By solving (10), the two problems above can be solved well.
For the case K>1, the likelihood equations can be solved with the conditional ML (CML) algorithm.
Furthermore, for solving the proposed model, simulation is combined with analytical method (conditional Monte Carlo simulation method).
In solving the example of paper, generalized Gaussian quadrature formula and conditional Monte Carlo simulation have been used, respectively.
Finally, the conditional reliability function, the conditional probability density and mean of the first-passage time of the optimally controlled system are obtained from solving the backward Kolmogorov equation and Pontryagin equation.
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