Open Access
Table 2.
Basic ABC model choice algorithm that aims at computing the posterior probabilities of statistical models in competition to explain the data.
| Input: |
| − xobs, the observed SED we want to analyse |
| − p(m), prior probability of the mth statistical model |
| − p(θm|m), prior distribution of parameter θm of the mth statistical model |
| − p(x|θm, m), probability density of a SED x given the mth statistical model, and the parameter θm, see Eq. (4) |
| − N, number of simulations from the prior |
| − S(x), a function that computes the summary statistics of a SED x |
| Output: |
An approximation  of the posterior probability of the mth statistical model given the observed data for all m. |
| 1 For i = 1 to N |
| 2 Generate mi from the prior p(m) |
3 Generate  from the prior p(θm|m) |
| 4 Generate xi from the model p(x|θm, m) |
5 Compute S(xi) and store  |
| 6 End For |
7 Compute  with Eq. (8) for all m |
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