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Manifest functions are the consequences that people see, observe or even expect. It is explicitly stated and understood by the participants in the relevant action. The manifest function of a rain dance, according to Merton in his 1957 Social Theory and Social Structure, is to produce rain, and this outcome is intended and desired by people participating in the ritual.
The use of latent variables can serve to reduce the dimensionality of data. Many observable variables can be aggregated in a model to represent an underlying concept, making it easier to understand the data. In this sense, they serve a function similar to that of scientific theories.
A latent variable model is a statistical model that relates a set of observable variables (also called manifest variables or indicators) [1] to a set of latent variables. Latent variable models are applied across a wide range of fields such as biology, computer science, and social science. [ 2 ]
The manifest function of education includes preparing for a career by getting good grades, graduation and finding good job. The second type of function is "latent functions", where a social pattern results in an unrecognized or unintended consequence.
Manifest functions are the consequences that people observe or expect, or what is intended; latent functions are those that are neither recognized nor intended. In distinguishing between manifest and latent functions, Merton argued that one must dig to discover latent functions.
Latent learning is the subconscious ... found intriguing evidence that the absence of a prion protein disrupts latent learning and other memory functions in the water ...
Intensive properties are material characteristics and are not dependent on the size or extent of the sample. Commonly quoted and tabulated in the literature are the specific latent heat of fusion and the specific latent heat of vaporization for many substances. From this definition, the latent heat for a given mass of a substance is calculated by
As it maps from a known input space to the low-dimensional latent space, it is called the encoder. The decoder is the second neural network of this model. It is a function that maps from the latent space to the input space, e.g. as the means of the noise distribution.