Supposing that the current values of the parameters at the mth iteration are and, the algorithm proceeds as follows.
For the E-step, the expected value of the log likelihood is calculated, with respect to the latent variable given the current values of the parameters.
Maximize the posterior distributions of β0 and β j (for all j) by substituting the fully conditional expectations for the current values of the parameters, one at the time.
At each step of the gradient descent, we had to sample a set of sequences by Gibbs sampling, under the current values of the parameters, so as to numerically estimate the gradient of the log-likelihood.
The position is the current value of the model parameter, while the velocity is a vector of numbers that are added to the position coordinates of the particle in order to move the particle from one time step to another.
The average GC-content induced by the current value of the x parameter can then be adequately estimated from the sample, or computed exactly using recent algorithmic advances (Ponty and Saule, 2011).
Since this OFDMA system is timeslotted, then each user must provide the current value of the QoS parameters (including rate, latency, and jitter) and the traffic transmission request for each type of traffic at the beginning of every timeslot.
Next we show – for the same experiment – both the QoS requirements (given by the application), as well as the current value of the QoS parameters computed by IFDS from the actual monitoring.
since (a_{i}^{(n)}(0)) (the ith input of the ANN) is equal to (beta _{i}^{(n)}) (the current value of the i th parameter).
In each iteration of an MCMC algorithm, a sample is taken from each conditional posterior distribution for each subset of the parameter space, given the current values of the other parameters.
