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We consider the problem of fitting given data (u_1,y_1),...,(u_m,y_m), where u_i in {bf R}^n and y_i in {bf R}, with a convex polynomial f$. A technique to solve this problem using sum of squares pol...
We consider the problem of fitting a convex piecewise-linear function, with some specified form, to given multi-dimensional data. Except for a few special cases, this problem is hard to solve exactly,...
Generalized additive models are an effective regression tool, popular in the statistics literature, that provide an automatic extension of traditional linear models to nonlinear systems. We pres...
Gaussian process (GP) models are commonly used statistical metamodels for emulating expensive computer simulators. Fitting a GP model can be numerically unstable if any pair of design points in the in...
Variational Bayes computational methods are attracting increasing in-terest because of their ability to scale to large data sets. Here, application of the non-conjugate variational message passing (N...
Ancestral graph models, introduced by Richard-son and Spirtes (2002), generalize both Markov random fields and Bayesian networks to a class of graphs with a global Markov property that is closed under...
The article investigated the application of a suitable estimation model to measure the productivity spill over effects of informal employees.
A series of ten plant species belonging to Magnoliopsida - Dicotyledons class were analyzed in terms of chemical compounds distribution of abundance, starting from the assumption that these distributi...
A factor effect study was conducted on a set of observations at the contingency of a series of plant species and bacteria species regarding the antibacterial activity of essential oil extracts.
While considerable advance has been made to account for statistical uncertainties in astronomical analyses, systematic instrumental uncertainties have been generally ignored. This can be crucial to a ...
Complex computer codes, for instance simulating physical phenomena, are often too time expensive to be directly used to perform uncertainty, sensitivity, optimization and robustness analyses. A wide...
In this article,we present a behind-the-scenes look at a Bayesian hierarchical analysis of pathways of exposure to arsenic(a toxic heavy metal) using the Phase I National Human Exposure Assessment ...
We use simulation studies, whose design is realistic for educational and medical research (as well as other elds of inquiry), to compare Bayesian and likelihood-based methods for tting variance-comp...
We study the problem of fitting circles (or circular arcs) to data points observed with errors in both variables. A detailed error analysis for all popular circle fitting methods – geometric fit, K&ar...
Local polynomial modelling can be seen as a local fit of the data against a polynomial basis. In this paper we extend this method to the power basis, i.e. a basis which consists of the powers of an ...

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