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Syntax & Psycho-Syntax

Demirdache, Hamida, Nicolas Guilliot · HAL (Le Centre pour la Communication Scientifique Directe) · 2011

International audience

Cited by 0

Syntax & Psycho-Syntax

Hamida Demirdache, Nicolas Guilliot · SPIRE - Sciences Po Institutional REpository · 2011

International audience

Cited by 0

Distinguishing syntax from para-syntax

James Dickins · 2020

Cited by 0

Music Syntax is Language Syntax *

David Pesetsky (mit, The &amp;quot;mozart Paradox · 2008

• Language and music are both law-governed, complex phenomena. Each reflects a body of knowledge and a set of cognitive abilities that structure experience and performance. • Much of this knowledge is &amp;quot;tacit&amp;quot;: information we have in our heads but are not aware of, unless we acquire the specialized knowledge of a linguist or music theorist. (1) Linguistic example: &amp;quot;island phenomena&amp;quot; a. To whom did you speak _ _ [before you left the room]? b. *To whom did you leave the room [before you spoke __]? vs. To whom did you say [you had spoken __]? (2) Musical example: consonance vs. dissonance a. some consonant intervals maj3 p5 p4 min3 min6 b. some dissonant intervals min2 min7 tritone maj2 maj7 • Music that most people will think of as supremely &amp;quot;consonant &amp;quot; is almost always full of &amp;quot;non-chordal &amp;quot; tones that generate dissonances with the current harmony. (3) Suspension

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Abstract syntax from concrete syntax

David S. Wile · 1997

Modem Software Engineering practice advocates the development of domain-specific specification languages to characterize formally the idioms of discourse and jargon of specific problem domains. With poorly-understood domains it is best to construct an abstract syntax to characterize the domain concepts and abstractions before developing a concrete syntax. Often, however, a good concrete syntax exists a priori: sometimes in sophisticated formal languages characterizing (often mathematical) domains but more often in miniature, legacy-code languages, sorely in need of reverse engineering. In such cases, it is necessary to derive an appropriate abstract syntax -or its first cousin, an object-oriented model -from the concrete syntax. This report describes a transformation process that produces a good abstract representation from a low-level concrete syntax specification.

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Syntax

Van Olmen, Daniel · Lancaster EPrints (Lancaster University) · 2022

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Syntax

Flore Geukens · OSF Preprints (OSF Preprints) · 2020

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Syntax

2011

functioning ” or “interpersonal comparability ” in ordinal survey responses. Given a selfassessment question (such as, “How healthy are you? Excellent, good, fair, or poor.”), different respondents may interpret the response categories in different ways, such that excellent health to one individual may be fair health to a hypochondriac. For each ordinal selfassessment to be corrected, the chopit model requires one or more vignette question (such as a description of a hypothetical person’s health, followed by the same response categories as the self-assessment), and a set of associated explanatory variables for the respondent. The key assumption of the approach is that the thresholds (which determine how respondents translate their views into the response categories) have the same effect for different questions asked of the same respondent, but may differ across respondents; the model uses a parametric specification to predict the thresholds associated with an individual. The self-assessment and vignette questions may be taken from different surveys, so long as both surveys include the same explanatory variable questions to predict the thresholds. For ordinal data (without

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Syntax

2011

Use the bivariate logistic regression model if you have two binary dependent variables (Y1, Y2), and wish to model them jointly as a function of some explanatory variables. Each pair of dependent variables (Yi1, Yi2) has four potential outcomes, (Yi1 = 1, Yi2 = 1), (Yi1 = 1, Yi2 = 0), (Yi1 = 0, Yi2 = 1), and (Yi1 = 0, Yi2 = 0). The joint probability for each of these four outcomes is modeled with three systematic components: the marginal Pr(Yi1 = 1) and Pr(Yi2 = 1), and the odds ratio ψ, which describes the dependence of one marginal on the other. Each of these systematic components may be modeled as functions of (possibly different) sets of explanatory variables.

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Syntax

2011

Use the Poisson regression model if the observations of your dependent variable represents the number of independent events that occur during a fixed period of time (see the negative binomial model, Section??, for over-dispersed event counts.) For a Bayesian implementation of this model, see Section??.

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Syntax

2011

Given several observed dependent variables and an unobserved explanatory variable, item response theory estimates the latent variable (ideal points). The model is estimated using the Markov Chain Monte Carlo algorithm via a Gibbs sampler and data augmentation. Use this model if you believe that the ideal points lie in one dimension, and see the k-dimensional item response model (Section??) for k hypothesized latent variables.

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Syntax

Otto Simonsson · Figshare · 2022

Syntax for dataset in this study.

Cited by 0Open access