We consider a semi-supervised setting for domain adaptation where only unlabeled data is available for the target domain. One way to tackle this problem is to train a generative model with latent variables on the mixture of data from the source and target domains. Such a model would cluster features in both domains and ensure that at least some of the latent variables are predictive of the label on the source domain.
Almost all research in the social and behavioral sciences, and also in eco
nomic and marketing research, criminological research, and social medical
research deals with the analysis of categorical data. Categorical data are
quantified as either nominal or ordinal variables. This volume is a collec
tion of up-to-date studies on modern categorical data analysis methods,
emphasizing their application to relevant and interesting data sets.
The previous section suggested that a long period of high government debt/GDP ratios may
increase uncertainty about the future path of interest rates, both real and nominal. Doubts
about how governments will respond probably increase uncertainty about inflation and,
perhaps, about future growth. Macroeconomic tail risks seem to have risen. At least much
market commentary suggests so – some talk about latent inflation risks while others fret
about deflation. The credibility of fiscal and monetary policy frameworks in the advanced
countries has been weakened by the crisis.