Overabundance and inflectional classification: Quantitative evidence from Czech

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Abstract

Overabundance is the situation where two or more distinct word forms fill the same cell in an inflectional paradigm (Thornton 2011). While this topic has received renewed attention in recent years, there are still several open questions regarding its properties and status. In this paper we present a new take on the matter. On the basis of a case study of the locative singular and instrumental plural of Czech nouns, we argue that there are at least two kinds of overabundance phenomena which should be distinguished, depending on whether overabundant behavior integrates in the inflection system or is orthogonal to it. The evidence for the distinction comes from a quantitative study of the way phonological, morphosyntactic, semantic, and sociolinguistic factors contribute to partially predicting whether a lexeme is overabundant and which form is used in different contexts.

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brms: An R Package for Bayesian Multilevel Models Using Stan

Paul-Christian Bürkner (2017)

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Stan: A Probabilistic Programming Language

Daniel Z Lee, Michael Betancourt, Marcus A. Brubaker … (2017)

Stan is a probabilistic programming language for specifying statistical models. A Stan program imperatively defines a log probability function over parameters conditioned on specified data and constants. As of version 2.14.0, Stan provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlo methods such as the No-U-Turn sampler, an adaptive form of Hamiltonian Monte Carlo sampling. Penalized maximum likelihood estimates are calculated using optimization methods such as the limited memory Broyden-Fletcher-Goldfarb-Shanno algorithm. Stan is also a platform for computing log densities and their gradients and Hessians, which can be used in alternative algorithms such as variational Bayes, expectation propagation, and marginal inference using approximate integration. To this end, Stan is set up so that the densities, gradients, and Hessians, along with intermediate quantities of the algorithm such as acceptance probabilities, are easily accessible. Stan can be called from the command line using the cmdstan package, through R using the rstan package, and through Python using the pystan package. All three interfaces support sampling and optimization-based inference with diagnostics and posterior analysis. rstan and pystan also provide access to log probabilities, gradients, Hessians, parameter transforms, and specialized plotting.