Inferring formal grammars with nonparametric Bayesian approach is one of the most powerful for achieving high accuracy from unsupervised data. In this paper, mildly-context-sensitive probabilities, called (k, l)-context-sensitive are defined on context-free (CFGs). CFGs where probabilities rules identified contexts can be seen as a kind dual approaches distributional learning, in which characte...

Data envelopment analysis (DEA) is an axiomatic, mathematical programming approach to productive efficiency analysis and performance measurement. This paper shows that DEA can be interpreted as a nonparametric least squares regression subject to shape constraints on production frontier and sign constraints on residuals. Thus, DEA can be seen as a nonparametric counter-part of the corrected ordi...

In randomized clinical trials, nonparametric approaches are considered when assumptions of a parametric approach are not reasonable. Nonparametric approaches have typically concentrated on hypothesis testing and, unlike parametric approaches, have not been amenable to providing measures of treatment efficacy. If X and Y denote the random variables representing the responses on two treatments A ...

Gaussian process state-space models (GP-SSMs) are a very flexible family of models of nonlinear dynamical systems. They comprise a Bayesian nonparametric representation of the dynamics of the system and additional (hyper-)parameters governing the properties of this nonparametric representation. The Bayesian formalism enables systematic reasoning about the uncertainty in the system dynamics. We ...

In this paper we present the results of a simulation study to explore the ability of Bayesian parametric and nonparametric models to provide an adequate fit to count data, of the type that would routinely be analyzed parametrically either through fixed-effects or random-effects Poisson models. The context of the study is a randomized controlled trial with two groups (treatment and control). Our...