Approximate inference by sampling from an appropriately constructed posterior has recently seen a dramatic increase in popularity in both the robotics and computer vision community. In this paper, I will describe a number of approaches in which my co-authors and I have used Sequential Monte Carlo methods and Markov chain Monte Carlo sampling to solve a variety of difficult and challenging infer...

This paper describes the methods and sample characteristics of a series of Spanish normative studies (The NEURONORMA project). The primary objective of our research was to collect normative and psychometric information on a sample of people aged over 49 years. The normative information was based on a series of selected, but commonly used, neuropsychological tests covering attention, language, v...

Two important tasks of machine learning are the statistical learning from sample data (SL) and the unsupervised learning from unlabelled data (UL) (Hastie et al., 2001; Theodoridis & Koutroumbas, 2006). The synthesis of the two parts – the unsupervised statistical learning (USL) – is frequently used in the cyclic process of inductive and deductive scientific inference. This applies especially t...

We propose a PAC-Bayes sample compression approach to kernel methods that can accommodate any bounded similarity function and show that the support vector machine (SVM) classifier is a particular case of a more general class of data-dependent classifiers known as majority votes of samplecompressed classifiers. We provide novel risk bounds for these majority votes and learning algorithms that mi...

In this paper, we compare the performance of two scenario-based numerical methods to solve stochastic optimal control problems: scenario trees and particles. The problem consists in finding strategies to control a dynamical system perturbed by exogenous noises so as to minimize some expected cost along a discrete and finite time horizon. We introduce the Mean Squared Error (MSE) which is the ex...

In this paper we discuss the application of a certain class of Monte Carlo methods to stochastic optimization problems. Particularly, we study variable-sample techniques, in which the objective function is replaced, at each iteration, by a sample average approximation. We first provide general results on the schedule of sample sizes, under which variable-sample methods yield consistent estimato...

Avian shell gland tissue was subjected to Western blot analysis using anti-human estrogen receptor-alpha antibody H222. Initial attempts to obtain consistent, high-quality blots were unsuccessful because, as it turned out, excessive lipid in tissue preparations interfered with protein separation. Incremental additions of acetone eventually proved to be the critical step in solubilizing lipids a...

In this paper, we propose a variable sample distributed algorithm for the computation of stochastic Nash equilibrium in which the objective functions are replaced, at each iteration, by sample average approximations. We investigate the contraction mapping properties of the variable sample distributed algorithm and show that the accuracy of estimators yielded in the algorithms to their true coun...

This article reviews the recent developments in Bio-Analytical sample preparation techniques and gives an update on basic principles, Applications and comparative discussion on the advantages and limitations of each technique. Conventional Solid-Phase Extraction (SPE), Molecularly Imprinted Polymer SPE techniques are have been considered as methods of past. Developments in SPE techniques such a...

Traditional image classification algorithms are mainly divided into unsupervised and supervised paradigms. In the first paradigm, algorithms are designed to automatically estimate the classes’ distributions in the feature space. The second paradigm depends on the knowledge of a domain expert to identify representative examples from the image to be used for estimating the classification model. R...