High dimensional models with parametric dependencies can be challenging to simulate. The computational e↵ort usually increases exponentially with the dimension of the parameter space. To keep the calculations feasible, one can use parametric model order reduction techniques. Multivariate Pad methods match higher order moments of the Laplace variable as well as the parameters. Interpolatory redu...

Analysis of execution traces plays a fundamental role in many program analysis approaches, such as runtime verification, testing, monitoring, and specification mining. Execution traces are frequently parametric, i.e., they contain events with parameter bindings. Each parametric trace usually consists of many meaningful trace slices merged together, each slice corresponding to one parameter bind...

With the exception of Bayesian analysis, phylogenetic inference procedures typically identify a best estimate of phylogenetic relationships, a so called point estimate of the phylogeny. However, the point estimate is often relatively uninteresting in itself unless we have some measure of its reliability. This lecture will be about techniques for examining the robustness or significance of the r...

In this paper, we introduce improvements to previous sines + transients + noise audio modeling systems, including new sinusoidal trajectory selection and quantization procedures. In previous work [1], the audio is first segmented into transient and non-transient regions. The transient region is modeled using traditional transform coding techniques, while the non-transient regions are modeled us...

We present ParaMetric, an automatic evaluation metric for data-driven approaches to paraphrasing. ParaMetric provides an objective measure of quality using a collection of multiple translations whose paraphrases have been manually annotated. ParaMetric calculates precision and recall scores by comparing the paraphrases discovered by automatic paraphrasing techniques against gold standard alignm...

A nonparametric model-assisted survey estimator for status estimation based on local polynomial regression is extended to incorporate spatial auxiliary information. Under mild assumptions, this estimator is design-unbiased and consistent. Simulation studies show that the nonparametric regression estimator is competitive with standard parametric techniques when a parametric specification is corr...

In this paper, we present a new non-parametric generalized formulation to statistical pressure snakes. We discuss the shortcomings of the traditional pressure snakes. We then introduce a new generic pressure model that alleviates these shortcomings, based on the Bayesian decision theory. Non-parametric techniques are used to obtain the statistical models that drive the snake. We discuss the adv...

We address the problem of automatic analysis of parametric counter and clock automata. We propose a semi-algorithmic approach based on using (1) expressive symbolic representation structures called Parametric DBM's, and (2) accurate extrapolation techniques allowing to speed up the reachability analysis and help its termination. The techniques we propose consist in guessing automatically the ee...

In this paper, a new non-parametric generalized formulation to statistical pressure snakes is presented. We discuss the shortcomings of the traditional pressure snakes. We then introduce a new generic pressure model that alleviates these shortcomings, based on the Bayesian decision theory. Non-parametric techniques are used to obtain the statistical models that drive the snake. We discuss the a...