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ω i increases the variance of likelihood in CI fusion.
Then, the variance of likelihood in each sensor becomes ({sigma ^{2}_{i}}/omega _{i}) in CI algorithm.
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The variance of maximum likelihood estimates (MLE) in the presence of type II right-censoring is almost un-degraded if there also is heavy left-censoring when estimating certain parameters.
The optimal change-time for the step PALT is determined by minimizing either the generalized asymptotic variance of maximum likelihood estimates (MLEs) of the acceleration factor and the hazard rate at use condition or the asymptotic variance of MLE of the acceleration factor.
In our analysis, they are set to the mean and variance of the likelihood functions of the target and the noise, respectively.
Hence, the variance of each likelihood in CI fusion has turned into ({sigma ^{2}_{i}}/omega _{i}) in the process of calculating the posterior distribution.
The variance of this likelihood was held constant throughout all experiments.
To identify the most probable number of ancestral populations, we investigated the mean and variance of the likelihood plots of the data for different K-values (i.e., plot of ln Pr(X/K) vs. K).
In order to confirm that our Bayesian analyses converged and mixed well, we monitored the likelihood scores and compared means and variances of all likelihood parameters and likelihood scores from the independent runs using the program Tracer v1.4 [ 34].
Burn-in was assessed by comparing the mean and variance of log likelihoods, both using the program Tracer ver. 1.4 [49] and by eye.
In order to confirm that the chains converged and mixed well, we monitored the likelihood scores and compared means and variances of all likelihood parameters from independent runs using Tracer v1.4.1 [ 45].
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