Table A.1
Results of the expectation-maximization algorithm and the AIC applied to the glitch size distribution modeled as a sum of Gaussians.
| Parameters | 1-G | 2-G | 3-G | 4-G |
|
|
||||
| α 1 | 1 | 0.83 | 0.36 | 0.27 |
| μ 1 | –1.02 | –1.51 | –2.62 | –2.72 |
| σ 1 | 1.65 | 1.37 | 0.79 | 0.73 |
| α 2 | ... | 0.17 | 0.47 | 0.26 |
| μ 2 | ... | 1.37 | –0.61 | –1.48 |
| σ 2 | ... | 0.13 | 0.13 | 1.17 |
| α 3 | ... | ... | 0.17 | 0.30 |
| μ 3 | ... | ... | 1.38 | –0.41 |
| σ 3 | ... | ... | 0.13 | 1.01 |
| α 4 | ... | ... | ... | 0.17 |
| μ 4 | ... | ... | ... | 1.38 |
| σ 4 | ... | ... | ... | 0.13 |
| AIC | 1437.81 | 1309.42 | 1297.17 | 1303.48 |
| Δm | 140.64 | 12.25 | 0 | 6.31 |
| w m | 10-31 | 0.002 | 0.957 | 0.040 |
Notes. The label m-G in each column represents the model m (i.e., a sum of m Gaussians). { αj,μj,σj } correspond to the weight, mean, and the standard deviation of the jth component of the sum of Gaussians, respectively. { AIC, Δm,wm } denote the AIC value, the relative AIC respect to the model with the minimum AIC value, and the Akaike weights of the model m, respectively.
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