The purpose of this article is to formulate a number of probabilis-tic hidden-variable theorems, to provide proofs in some cases, and counterexamples to some conjectured relationships. The first theorem is the fundamental one. It asserts the general equivalence of the existence of a hidden variable and the existence of a joint probability distribution of the observable quantities, whether finit...

In this work, synthesis of facial animation is done by modelling the mapping between facial motion and speech using the shared Gaussian process latent variable model. Both data are processed separately and subsequently coupled together to yield a shared latent space. This method allows coarticulation to be modelled by having a dynamical model on the latent space. Synthesis of novel animation is...

Cognitive Diagnosis Models (CDMs) are useful statistical tools in cognitive diagnosis assessment. However, as many other latent variable models, the CDMs often suffer from the non-identifiability issue. This work gives the sufficient and necessary condition for the identifiability of the basic DINA model, which not only addresses the open problem in Xu and Zhang (2016, Psychomatrika, 81:625-649...

We consider in this paper the robustness of decisions based on probabilistic thresholds. To this effect, we propose the same-decision probability as a query that can be used as a confidence measure for threshold-based decisions. More specifically, the same-decision probability is the probability that we would have made the same threshold-based decision, had we known the state of some hidden var...

OBJECTIVE To estimate the relationship between variations in medical spending and health outcomes of the elderly. DATA SOURCES 1992-2002 Medicare Current Beneficiary Surveys. STUDY DESIGN We used instrumental variable (IV) estimation to identify the relationships between alternative measures of elderly Medicare beneficiaries' medical spending over a 3-year observation period and health stat...

We propose a latent variable approach for multiple clustering of categorical data. We use logistic regression models for the conditional distribution of observable features given the latent cluster variables. This model supports an interpretation of the different clusterings as representing distinct, independent factors that determine the distribution of the observed features. We apply the mode...

Two methods are proposed for correct and verifiable geometric reasoning using finite precision arithmetic. The first method, data normalization, transforms the geometric structure into a configuration for which all finite precision calculations yield correct answers. The second method, called the hidden variable method, constructs configurations that belong to objects in an infinite precision d...

In this paper we describe a method for automatically animating interactive characters based on an existing corpus of key-framed hand-animation. The method learns separate low-dimensional embeddings for subsets of the hand-animation corresponding to different semantic labels. These embeddings use the Gaussian Process Latent Variable Model to map high-dimensional rig control parameters to a three...

Facial-expression data often appear in multiple views either due to head-movements or the camera position. Existing methods for multi-view facial expression recognition perform classification of the target expressions either by using classifiers learned separately for each view or by using a single classifier learned for all views. However, these approaches do not explore the fact that multi-vi...

Linear latent structure analysis is a new approach for investigation of population heterogeneity using high-dimensional categorical data. In this approach, the population is represented by a distribution of latent vectors, which play the role of heterogeneity variables, and individual characteristics are represented by the expectation of this vector conditional on individual response patterns. ...