**Graphical Model**

A graphical model is a probabilistic model for which a graph denotes the conditional dependence structure between random variables. They are commonly used in probability theory, statistics – particularly Bayesian statistics – and machine learning. Generally, probabilistic graphical models use a graph-based representation as the foundation for encoding a complete distribution over a multi-dimensional space and a graph that is a compact or factorized representation of a set of independences that hold in the specific distribution. Two branches of graphical representations of distributions are commonly used, namely, Bayesian networks and Markov networks. Both families encompass the properties of factorization and independences, but they differ in the set of independences they can encode and the factorization of the distribution that they induce. … **Edgeworth Series**

The Gram-Charlier A series (named in honor of Jørgen Pedersen Gram and Carl Charlier), and the Edgeworth series (named in honor of Francis Ysidro Edgeworth) are series that approximate a probability distribution in terms of its cumulants. The series are the same; but, the arrangement of terms (and thus the accuracy of truncating the series) differ.

http://…/EdgeworthSeries.html … **Semantic Differential**

Semantic differential is a type of a rating scale designed to measure the connotative meaning of objects, events, and concepts. The connotations are used to derive the attitude towards the given object, event or concept.

Osgood’s semantic differential was an application of his more general attempt to measure the semantics or meaning of words, particularly adjectives, and their referent concepts. The respondent is asked to choose where his or her position lies, on a scale between two bipolar adjectives (for example: “Adequate-Inadequate”, “Good-Evil” or “Valuable-Worthless”). Semantic differentials can be used to measure opinions, attitudes and values on a psychometrically controlled scale. …

# If you did not already know

**27**
*Friday*
Oct 2017

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