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Regularized kernel methods such as support vector machines (SVM) and support vector regression (SVR) constitute a broad and flexible class of methods which are theoretically well investigated and comm...
Assessing variability according to distinct factors in data is a fundamental technique of statistics. The method commonly regarded to as analysis of variance (ANOVA) is, however, typically confined to...
Sequential Monte Carlo techniques are useful for state estimation in non-linear, non-Gaussian dy-namic models. These methods allow us to ap-proximate the joint posterior distribution using sequential ...
The Bernoulli Factory is an algorithm that takes as input a series of i.i.d. Bernoulli random variables with an unknown but fixed success probability $p$, and outputs a corresponding series of Bernoul...
We examine the use of a structured thresholding algorithm for sparse underwater channel estimation using compressed sensing. This method shows some improvements over standard algorithms for sparse ch...
The paper argues that changing skill mix is not a panacea for all the ills of an organisation. It has a role to play in improving organisational effectiveness and quality of care, but it must be re...
Academic philosophers have recently turned their attention to practical areas such as law, medicine, business, etc., and, in particular to moral problems that may arise in these areas. But there are a...
The paper is divided into six sections. In the first, the different types of migration are reviewed. Some global trends are depicted in the second section. Scarcity of data on health worker migration ...
The effect of vigorous physical activity on mortality in the elderly is difficult to estimate using conventional approaches to causal inference that define this effect by comparing the mortality risks...
This is a refreshing and stimulating book and it very much deserves a wider audience than I suspect it will get. Although it targets the MBA market and ‘those who want to update their management skill...
Walley’s Imprecise Dirichlet Model (IDM) for categorical i.i.d. data extends the classical Dirichlet model to a set of priors. It overcomes several fundamental problems which other approaches to unc...
Walley’s Imprecise Dirichlet Model (IDM) for categorical i.i.d. data extends the classical Dirichlet model to a set of priors. It overcomes several fundamental problems which other approaches to unc...
Practical wavelet design on the sphere。

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