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A recent trend in the development of transfer learning solutions is for solutions to address both marginal and conditional distribution differences between the source and target domains.
The previous solutions surveyed address domain adaptation by correcting for marginal distribution differences, correcting for conditional distribution differences, or correcting for both marginal and conditional distribution differences.
The second proposed solution is the two stage weighting framework for multi-source domain adaptation (2SW-MDA) which addresses both marginal and conditional distribution differences between the source and target domains.
Note that, when the information about the total population is available, both marginal and conditional exchangeability assumptions are met in Figure 4; the distributions of DS response types are comparable between the exposed and unexposed groups.
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Mardia's type I multivariate Pareto distribution has the attractive feature that both marginals and conditional distributions are Paretian in nature.
Lastly, the surveyed works of Long [68], Xia [132], Chattopadhyay [14], Duan [28], and Long [69] correct the differences in both the marginal and conditional distributions.
The majority of the homogeneous transfer learning solutions employ one of three general strategies which include trying to correct for the marginal distribution difference in the source, trying to correct for the conditional distribution difference in the source, or trying to correct both the marginal and conditional distribution differences in the source.
It has been suggested that a fuller picture of the treatment effect can be obtained from the application of both the marginal and conditional models [ 14, 37].
In both cases, the marginal and conditional effects of BISS depression were similar, while the effect of BISS irritability decreased when adjusting for BISS depression.
Model conditions were assessed through analysis of marginal and conditional residuals.
Inference from such a BN needs to specify a great number of marginal and conditional probabilities.
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