It follows from the mathematical statistics that if quantities [[eta].sub.1], [[eta].sub.2], ..., [[eta].sub.n] are independent of one another in events of a subensemble and obey Gaussian distributions
, the distribution of these parametrically invariant quantities does not depend on the parameters of the Gaussian distributions
, and the number n of particles in the subensemble event uniquely determines the distribution of parametrically invariant quantities.
The local output of each node is the local posterior approximated as a Gaussian distribution
. On the contrary, in the case that the measurement is not available to the node i, then only the prediction of the particle flow filter will be executed (lines 1-3 of Algorithm 1).
State mode The shockwave pattern The boundary condition I forward wave forward wave [[omega].sub.f] [[omega].sub.f] (2) (1) input flow rate [q.sub.in] II backward wave output flow rate [[omega].sub.b] [q.sub.max] input flow rate [q.sub.in] III forward wave output flow rate [[omega].sub.f] (2) [q.sub.max] input flow rate [q.sub.in] IV forward wave output flow rate 0 input [[omega].sub.f] (2) flow rate [q.sub.in] V backward wave (2) output flow rate 0 input [[omega].sub.b] (2) flow rate [q.sub.in] State mode Signal Traffic state phase I green Free flow-Free flow II green Free flow-Congestion2 III green Free flow-Congestion1 IV red Free flow-Congestion1 V red Free flow-Congestion2 Table 3: Fitted Gaussian distribution
of shockwave speed (feet/s).
Closed-form expressions of probability of false alarm and probability of miss-detection for the adaptive weighing algorithm with a p-norm detector were computed under the assumption of a Gamma or Gaussian distribution
of the test statistic.
Besides that, we integrated the global Gaussian distributions
information into a conventional edge-based level set model, proposed in .
And this distribution is a Gaussian distribution
The value of the average dental proportion parameter p was computed for each individual and it was tested whether or not the distribution of this parameter in the sample could be described by a Gaussian distribution
or a mixture of Gaussian distributions
Older laser technologies created a Gaussian distribution
of intensity with decreasing energy distribution toward the perimeter of the laser field.
In the article titled "Comparison of some tests of fit for the Inverse Gaussian distribution
" , there were a number of typographical and other errors.
Under the null hypothesis [H.sub.0], corresponding to the absence of any cluster, all the marks come from the same Gaussian distribution