This paper presents a Bayesian latent variable model used to analyze ordinal response survey data by taking into account the characteristics of respondents. The ordinal response data are viewed as multivariate responses arising from continuous latent variables with known cut-points. Each respondent is characterized by two parameters that have a Dirichlet process as their joint prior distributio...

We investigate the relation between the local variables of a discrete integrable lattice system and the corresponding separation variables, derived from the associated spectral curve. In particular, we have shown how the inverse transformation from the separation variables to the discrete lattice variables may be factorised as a sequence of canonical transformations, following the procedure out...

For simplicity we will focus on a simple Gaussian Mixture Model. Consider a mixture of k spherical gaussians in R which is the following generative process. Let w ∈ ∆([k]) denote a distribution and let μ1, . . . , μk ∈ R be the mean vectors. Each point xi is generated by first choosing a component hi ∼ w and then xi ∼ N (μhi , I). We are given n samples x1, . . . , xn drawn according to this pr...

Unobservable variables in econometrics are represented in one of three ways: by variables contaminated by measurement errors, by proxy variables, or by various manifest indicators and/or causes. This paper contains a discussion of models involving each of these representations, and highlights certain interesting implications that have been ‘insufficiently emphasized or completely unrecognized i...

We propose a solution to the challenge of the CoNLL 2008 shared task that uses a generative history-based latent variable model to predict the most likely derivation of a synchronous dependency parser for both syntactic and semantic dependencies. The submitted model yields 79.1% macroaverage F1 performance, for the joint task, 86.9% syntactic dependencies LAS and 71.0% semantic dependencies F1....

Instrumental variable (IV) estimation typically requires the user to correctly specify the relationship between the regressors and the outcome to obtain a consistent estimate of the effects of the treatments. This paper proposes doubly robust IV regression estimators that only require the user to either correctly specify the relationship between the measured confounding variables (i.e., include...

Existing models for cluster analysis typically consist of a number of attributes that describe the objects to be partitioned and one single latent variable that represents the clusters to be identified. When one analyzes data using such a model, one is looking for one way to cluster data that is jointly defined by all the attributes. In other words, one performs unidimensional clustering. This ...