In this case of l n,p -dependent observation variables, it is possible to study decision rules for exact inference on scaling parameters as before.
The analysis of a set of markers with a high missing rate from the GENEVA project on prematurity shows that exact inference on equilibrium can be altered considerably when missingness is taken into account.
Under this strong assumption, exact inference for the covariate effects is straightforward.
In the latter scenario, the APX hardnessb result essentially implies that exact inference, being of exponential complexity, is no longer feasible.
However, exact inference and learning of the above model is computationally intractable, because of non-linear relationship between latent variables and parameters.
Secondly, for the problem of computational intractability of exact inference, we derive two approximate inference algorithms by factorizing the belief state based on the marginal and conditional independence assumptions.
In their case, exact inference was feasible because of the small size of the GO-DAG part used in the study (105 terms).
Thus the results of exact inference in the compiled model are valid also for the detailed one.
Bolzano proves that the premises of an exact inference are independent and one can prove that in cases of exact deducibility, the premises and the conclusion must share at least one variable (see George, 1983).
By doing this, the tree width of the model is always limited to a small number regardless of the total number of components (regions), making exact inference by variable elimination tractable.
