We consider the modelling of dependent defaults in large credit portfolios using latent variable models (the approach that underlies KMV and CreditMetrics) and mixture models (the approach underlying CreditRisk). We explore the role of copulas in the latent variable framework and show that for given default probabilities of individual obligors the distribution of the number of defaults in the p...

We develop a probabilistic latent-variable model to discover semantic frames—types of events or relations and their participants—from corpora. Our key contribution is a model in which (1) frames are latent categories that explain the linking of verb-subject-object triples in a given document context; and (2) cross-cutting semantic word classes are learned, shared across frames. We also introduc...

Restricted Boltzmann Machines (RBMs) are an important class of latent variable models for representing vector data. An under-explored area is multimode data, where each data point is a matrix or a tensor. Standard RBMs applying to such data would require vectorizing matrices and tensors, thus resulting in unnecessarily high dimensionality and at the same time, destroying the inherent higher-ord...

This teaching note starts with a demonstration of a straightforward procedure using Mplus Version 6 to produce a bias-corrected (BC) bootstrap confidence interval for testing a specific mediation effect in a complex latent variable model. The procedure is extended to constructing a BC bootstrap confidence interval for the difference between two specific mediation effects. The extended procedure...

In this paper we apply the latest techniques in sparse Gaussian process regression (GPR) to the Gaussian process latent variable model (GPLVM). We review three techniques and discuss how they may be implemented in the context of the GP-LVM. Each approach is then implemented on a well known benchmark data set and compared with earlier attempts to sparsify the model.

We wish to congratulate the authors for their innovative contribution, which is bound to inspire much further research. We find latent variable model selection to be a fantastic application of matrix decomposition methods, namely, the superposition of low-rank and sparse elements. Clearly, the methodology introduced in this paper is of potential interest across many disciplines. In the followin...

Deep latent variable models are powerful tools for representation learning. In this paper, we adopt the deep information bottleneck model, identify its shortcomings and propose a model that circumvents them. To this end, we apply a copula transformation which, by restoring the invariance properties of the information bottleneck method, leads to disentanglement of the features in the latent spac...

The prosody of speech is closely related to syntactic structure of the spoken sentence, and thus analysis models that jointly consider these two types of information are promising. However, manual annotation of syntactic information and prosodic information such as pauses is laborious, and thus it can be difficult to obtain sufficient data to train such joint models. In this paper, we tackle th...

We introduce a novel variational method that allows to approximately integrate out kernel hyperparameters, such as length-scales, in Gaussian process regression. This approach consists of a novel variant of the variational framework that has been recently developed for the Gaussian process latent variable model which additionally makes use of a standardised representation of the Gaussian proces...

The purpose of this paper is to review major statistical and psychometric issues impacting the study of psychophysiological reactivity and discuss their implications for applied developmental researchers. We first cover traditional approaches such as the observed difference score (DS) and the observed residual score (RS), including a review of classic and recent research on their reliability an...