For j in {1, … , p} update the variance of marker effects with a draw from a scaled-inverse chi-square density with scale and degree of freedom parameters β j 2 + d. f. β × S β and 1 + d. f. β, respectively, where S β and d. f. β are the prior scale and prior degree of freedom assigned to the variances of marker effects.
The SNP effects were treated as fixed effects such that it was not required to update the variance covariance matrix (V) nor invert V for each set of sampled QTL effects, which made it possible to carry out a large number of RJMCMC rounds.
Sensor j needs to communicate only with its single-hop neighbors in (mathcal {N}_{j}), in order to evaluate the coefficients of the polynomials in (9) and (10) and to update the noise variance estimates in (12).
In case of a fully observed Gaussian phenotype, update the error variance σ 0 2 into its conditional expectation σ 0 2 : = 1 n − 2 ∑ i = 1 n (y i − β 0 − ∑ j = 1 p β j x i j ) 2, for n > 2. Update the effect variances σ j 2 (for all j) to their conditional expectations.
In case of a fully observed Gaussian phenotype, update the error variance σ 0 2 into its fully conditional expectation σ 0 2 : = 1 n − 2 (y − β 0 − Z u ) ′ (y − β 0 − Z u ).
Step 3: The last step updates the variance.
Moreover, Scott et al. [26] updated the mean and variance information by Kalman filter updating equations for maintaining the modeled background.
After each iteration of the equalizer, we update the means and the variances using the soft estimated symbols.
The new a posteriori LLR permits to update the means and the variances of the estimated symbols as in [7, 13]: (4).
It can be seen that all node estimates x ̄ n ( k, J ) converge to the same value and follow the target state, as asserted by Theorem 1. Figure 7b and 7c shows the node estimates x ̄ n ( k, J ) in the consensus update after the twenty-first tracking update and the variance of all the node estimates, respectively.
