SMC students re affirm ed the election of Kathy Barlow and Misssy U nderm an for student body p re s id e n t an d v icepresident in F rid ay ’s balloting at St. M ary’s . In the election held to clarify a nine vote discrepancy which was discorded in the first totals, Barlow and U nderm an collected 606 votes to defeat Jan e Sheehy and Sue Welte who polled 491 votes. P leased with the large tur...

Finding the global minimum of a function is often difficult. We consider efficiently minimizing functions which are computationally expensive to evaluate. A Bayesian approach to the global function optimization problem places a prior distribution on the function and chooses where to evaluate the function based on its posterior distribution given a set of observations. While many recent applicat...

The lasso, introduced by Robert Tibshirani in 1996, has become one of the most popular techniques for estimating Gaussian linear regression models. An important reason for this popularity is that the lasso can simultaneously estimate all regression parameters as well as select important variables, yielding accurate regression models that are highly interpretable. This paper derives an efficient...

This paper presents a novel methodology to infer parameters of probabilistic models whose output noise is a Student-t distribution. The method is an extension of earlier work for models that are linear in parameters to nonlinear multi-layer perceptrons (MLPs). We used an EM algorithm combined with variational approximation, the evidence procedure, and an optimisation algorithm. The technique wa...

In the Gaussian process regression the observation model is commonly assumed to be Gaussian, which is convenient in computational perspective. However, the drawback is that the predictive accuracy of the model can be significantly compromised if the observations are contaminated by outliers. A robust observation model, such as the Student-t distribution, reduces the influence of outlying observ...

University researchers and teacher facilitators implemented a state-funded professional development project during the 2005-06 academic year to help county middle school teachers improve student achievement in mathematics. In this paper, we discuss lessons and results from this innovative model, whose iterative cycle includes teacher content knowledge, item analysis from a high-stakes test, ped...

The original Kohonen's Self-Organizing Map model has been extended by several authors to incorporate an underlying probability distribution. These proposals assume mixtures of Gaussian probability densities. Here we present a new self-organizing model which is based on a mixture of multivariate Student-t components. This improves the robustness of the map against outliers, while it includes the...

This note presents the package AdMit (Ardia et al., 2008, 2009), an R implementation of the adaptive mixture of Student-t distributions (AdMit) procedure developed by Hoogerheide (2006); see also Hoogerheide et al. (2007); Hoogerheide and van Dijk (2008). The AdMit strategy consists of the construction of a mixture of Student-t distributions which approximates a target distribution of interest....

This paper deals with Dynamic Stochastic General Equilibrium (DSGE) models under a multivariate student-t distribution for the structural shocks. Based on the solution algorithm of Klein (2000) and the gamma-normal representation of the t -distribution, the TaRB-MH algorithm of Chib and Ramamurthy (2010) is used to estimate the model. A technique for estimating the marginal likelihood of the DS...

Bayesian optimization has recently attracted the attention of the automatic machine learning community for its excellent results in hyperparameter tuning. BO is characterized by the sample efficiency with which it can optimize expensive black-box functions. The efficiency is achieved in a similar fashion to the learning to learn methods: surrogate models (typically in the form of Gaussian proce...