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In an EAV data model, each attribute–value pair is a fact describing an entity, and a row in an EAV table stores a single fact. EAV tables are often described as "long and skinny": "long" refers to the number of rows, "skinny" to the few columns. Data is recorded as three columns: The entity: the item being described.
Row headers are identified by ! scope="row" | instead of |. Each header cell should be on a separate line in the wiki-markup. The scope="col" and scope="row" markup ...
The following table classifies the various simple data types, associated distributions, permissible operations, etc. Regardless of the logical possible values, all of these data types are generally coded using real numbers, because the theory of random variables often explicitly assumes that they hold real numbers.
Moreover, the final row and the final column give the marginal probability distribution for A and the marginal probability distribution for B respectively. For example, for A the first of these cells gives the sum of the probabilities for A being red, regardless of which possibility for B in the column above the cell occurs, as 2 / 3 .
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Rectified Gaussian distribution a rectified version of normal distribution with all the negative elements reset to 0 Complex normal distribution deals with the complex normal vectors. A complex vector X ∈ C k is said to be normal if both its real and imaginary components jointly possess a 2 k -dimensional multivariate normal distribution.
Weighted least squares (WLS), also known as weighted linear regression, [1] [2] is a generalization of ordinary least squares and linear regression in which knowledge of the unequal variance of observations (heteroscedasticity) is incorporated into the regression.
Word2vec is a group of related models that are used to produce word embeddings.These models are shallow, two-layer neural networks that are trained to reconstruct linguistic contexts of words.