Table C.1
p-value of the K-S statistic for all marginalised posterior distributions from retrievals of scattered instances of SP-TW.
| log10(pKS) | ||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Rp | CO2 | CO | H2O | CH4 | H2S | SO2 | HCN | C2H2 | pcloud | Tp0 | Tp1 | Tp2 | Tp3 | |
| 0 | −45.1 | — | −76.0 | — | −138.9 | — | −144.0 | −59.4 | — | −1.5 | −16.9 | −23.5 | — | −10.3 |
| 1 | −40.0 | — | −75.5 | — | −141.2 | — | −145.4 | −55.0 | — | −1.9 | −16.9 | −24.5 | — | −9.6 |
| 2 | −41.3 | — | −77.8 | — | −141.1 | — | −140.3 | −51.5 | — | −1.5 | −17.1 | −25.0 | — | −9.8 |
| 3 | −41.4 | — | −76.0 | — | −142.0 | — | −141.0 | −54.0 | — | −2.7 | −16.2 | −22.6 | — | −10.0 |
| 4 | −44.1 | — | −78.1 | — | −140.2 | — | −142.2 | −57.2 | — | −2.2 | −16.1 | −24.1 | — | −10.3 |
| 5 | −42.6 | — | −75.1 | — | −141.5 | — | −144.1 | −52.9 | — | −1.7 | −20.1 | −23.3 | — | −10.5 |
| 6 | −43.0 | — | −75.2 | — | −141.0 | — | −143.3 | −53.6 | — | −1.7 | −18.9 | −24.2 | — | −9.7 |
| 7 | −43.7 | — | −76.6 | — | −140.5 | — | −140.7 | −54.9 | — | −1.5 | −19.3 | −23.1 | — | −10.4 |
| 8 | −39.2 | — | −74.7 | — | −140.7 | — | −144.5 | −54.4 | — | −1.2 | −20.4 | −22.1 | − | −10.3 |
| 9 | −43.1 | — | −77.3 | — | −144.2 | — | −139.6 | −54.3 | — | −1.8 | −16.6 | −22.4 | — | −10.4 |
Notes. Two-sample K-S statistics are calculated between the marginalised posteriors of the original instance of SP-TW and of the scattered instances for each parameter. We have used the resample_equal() method as implemented in the nestle Python package to resample the samples to have equal weights1. Values reported in the table are the base-10 logarithm of the p-value associated with the K-S statistic, where empty entries correspond to values of pKS = 0.
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