Open Access
Table 3.
Machine-learning-based ABC model choice algorithm that computes the posterior probability of two statistical models in competition to explain the data.
| Input and output: same as Table 2 | |
| 1 | Generate N simulations  from the joint distribution p(m)p(θm|m)p(x|θm, m) |
| 2 | Summarize all simulated datasets (photometric SED) xi with S(xi) and store all simulated  into a large catalog |
| 3 | Split the catalog into three parts: training, validation, and test catalogs |
| 4 | Fit each machine-learning method on the training and validation catalogs to approximate p(m = 1|S(x)) with  |
| 5 | Choose the best machine-learning method by comparing their classification errors on the test catalog |
| 6 | Return the approximation  computed with the best method |
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.