Figure 2

Performance evaluation for linear parameter estimation. A: Root mean square error of parameter V given parameter K = 2 was plotted while variance of the noise increased from 0.01 to 100 with both MCMC and least square. The true value of V was set as 5. Root mean square error monotonically increased as variance increased. The root mean square error was calculated as 0.0331 and 0.0256 as variance of noise increased to 100 for MCMC and least square, respectively. This demonstrated that the linear parameter estimation performed within the recommended range. B: the boxplot for the estimation of V were shown for both least square and MCMC methods as the noise variances increased from 0 to 100. The number of outliers is significantly higher for MCMC comparing to least square.