We estimate the magnitude of social interaction effects in disability pension participation in Norway. Specifically, we investigate how a worker’s propensity to draw disability benefits is affected by a plausibly exogenous shock to the disability entry rate of similarly-aged workers in his neighborhood. The problem of omitted variable bias is addressed employing a novel instrumental variable (I...

We describe a new method for multivariate discretization based on the use of a latent variable model. The method is proposed as a tool to extend the scope of applicability of machine learning algorithms that handle discrete variables only.

Writing comments about news articles, blogs, or reviews have become a popular activity in social media. In this paper, we analyze reader comments about reviews. Analyzing review comments is important because reviews only tell the experiences and evaluations of reviewers about the reviewed products or services. Comments, on the other hand, are readers’ evaluations of reviews, their questions and...

In a causal graphical model, an instrument for a variable X and its effect Y is a ran­ dom variable that is a cause of X and in­ dependent of all the causes of Y except X (Pearl 1995). For continuous variables, in­ strumental variables can be used to estimate how the distribution of an effect will respond to a manipulation of its causes, even in the presence of unmeasured common causes (con­ fo...

In this paper we describe a methodology for model-based single channel separation of sounds. We present a sparse latent variable model that can learn sounds based on their distribution of time/frequency energy. This model can then be used to extract known types of sounds from mixtures in two scenarios. One being the case where all sound types in the mixture are known, and the other being being ...

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...

We propose a perceptron-style algorithm for fast discriminative training of structured latent variable model, and analyzed its convergence properties. Our method extends the perceptron algorithm for the learning task with latent dependencies, which may not be captured by traditional models. It relies on Viterbi decoding over latent variables, combined with simple additive updates. Compared to e...

Lifted probabilistic inference methods exploit symmetries in the structure of probabilistic models to perform inference more efficiently. In lifted variable elimination, the symmetry among a group of interchangeable random variables is captured by counting formulas, and exploited by operations that handle such formulas. In this paper we generalize the structure of counting formulas and present ...

When used as a surrogate objective for maximum likelihood estimation in latent variable models, the evidence lower bound (ELBO) produces state-of-the-art results. Inspired by this, we consider the extension of the ELBO to a family of lower bounds defined by a particle filter’s estimator of the marginal likelihood, the filtering variational objectives (FIVOs). FIVOs take the same arguments as th...

Latent or hidden variable based statistical methods constitute a set of valuable techniques for modeling co-occurrence data, as they can effectively trade-off the simplicity of assuming independence among the features with the computational intractability of modeling a full joint distribution. Such models however are primarily “explanatory” rather than “predictive”. In particular, the “closed w...