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The Boltzmann Machine in Psychometrics: Implications and Applications
Presenters: Benjamin Deonovic (Corteva), Timo Bechger (Metior Consulting), Gunter Maris (Metior Consulting)

The Boltzmann machine remains an attractive generative model for supervised and unsupervised learning of complex
multivariate distributions of binary random variables. Often the observed variables are augmented with unobserved
binary variables and a Boltzmann machine is assumed for both together. Popular examples are restricted Boltzmann
machines, latent tree models, and deep Boltzmann machines. In this webinar we explore three topics related to the
use of Boltzmann machines in psychometrics: how to exploit the representation of a Boltzmann machine as a special
instance of Multidimensional Item Response Theory models (Maris and Bechger, 2021), how this representation sheds
light on the implications of recent work on the identifiability of the four parameter normal ogive (4PNO) model
through the approximation of a latent tree model (Maris, Bechger, and Deonovic 2020), and finally an application
of latent tree models to a large scale educational assessment aimed to provide personalized diagnostic feedback
(Deonovic, Bechger, and Maris 2020).

Nov 18, 2021 11:00 AM in Eastern Time (US and Canada)

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