We present an approach for pricing of illiquid bonds (and bond derivatives) in an arbitrage-free way and consistently with observed prices of liquid bonds. The basic model is a multifactor term structure model with abstract latent factors. The approach is based on stochastic ltering techniques, leading to a continuous update of the distribution of the latent factors on the basis of the informat...

In this work we introduce a new framework for performing temporal predictions in the presence of uncertainty. It is based on a simple idea of disentangling components of the future state which are predictable from those which are inherently unpredictable, and encoding the unpredictable components into a low-dimensional latent variable which is fed into a forward model. Our method uses a supervi...

We present a case study to demonstrate the possibility of discovering complex and interesting latent structures using hierarchical latent class (HLC) models. A similar effort was made earlier [6], but that study involved only small applications with 4 or 5 observed variables. Due to recent progress in algorithm research, it is now possible to learn HLC models with dozens of observed variables. ...

Among latent variables that can be used in e-collaboration research, job performance is a particularly important one. It measures what most e-collaboration tools in organizations aim to improve, namely the performance at work of individuals executing tasks collaboratively with others. We report on a comparative assessment of scores generated based on a self-reported job performance measurement ...

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Despite recent improvements in training methodology, discrete latent variable models have failed to achieve the performance and popularity of their continuous counterparts. Here, we evaluate several approaches to training large-scale image models on CIFAR-10 using a probabilistic variant of the recently proposed Vector Quantized VAE architecture. We find that biased estimators such as continuou...

Latent variable models, such as the popular Knowledge Tracing method, are often used to enable adaptive tutoring systems to personalize education. However, finding optimal model parameters is usually a difficult non-convex optimization problem when considering latent variable models. Prior work has reported that latent variable models obtained from educational data vary in their predictive perf...