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Title: Kernel estimators of asymptotic variance for adaptive Markov chain Monte Carlo |
Abstract: We study the asymptotic behavior of kernel estimators of asymptotic variances (or long-run variances) for a class of adaptive Markov chains. The convergence is studied both in $L^p$ and almost surely. The results also apply to Markov chains and improve on the existing literature by imposing weaker conditions.... |
Title: Sharp Dichotomies for Regret Minimization in Metric Spaces |
Abstract: The Lipschitz multi-armed bandit (MAB) problem generalizes the classical multi-armed bandit problem by assuming one is given side information consisting of a priori upper bounds on the difference in expected payoff between certain pairs of strategies. Classical results of (Lai and Robbins 1985) and (Auer et a... |
Title: Global sensitivity analysis for models with spatially dependent outputs |
Abstract: The global sensitivity analysis of a complex numerical model often calls for the estimation of variance-based importance measures, named Sobol' indices. Metamodel-based techniques have been developed in order to replace the cpu time-expensive computer code with an inexpensive mathematical function, which pred... |
Title: The relation between Pearson's correlation coefficient r and Salton's cosine measure |
Abstract: The relation between Pearson's correlation coefficient and Salton's cosine measure is revealed based on the different possible values of the division of the L1-norm and the L2-norm of a vector. These different values yield a sheaf of increasingly straight lines which form together a cloud of points, being the... |
Title: Machine Learning: When and Where the Horses Went Astray? |
Abstract: Machine Learning is usually defined as a subfield of AI, which is busy with information extraction from raw data sets. Despite of its common acceptance and widespread recognition, this definition is wrong and groundless. Meaningful information does not belong to the data that bear it. It belongs to the observ... |
Title: Belief Propagation and Loop Calculus for the Permanent of a Non-Negative Matrix |
Abstract: We consider computation of permanent of a positive $(N\times N)$ non-negative matrix, $P=(P_i^j|i,j=1,\cdots,N)$, or equivalently the problem of weighted counting of the perfect matchings over the complete bipartite graph $K_N,N$. The problem is known to be of likely exponential complexity. Stated as the part... |
Title: Co-word Analysis using the Chinese Character Set |
Abstract: Until recently, Chinese texts could not be studied using co-word analysis because the words are not separated by spaces in Chinese (and Japanese). A word can be composed of one or more characters. The online availability of programs that separate Chinese texts makes it possible to analyze them using semantic ... |
Title: Variable Second-Order Inclusion Probabilities as a Tool to Predict the Sampling Variance |
Abstract: A generalization of Gy's theory for the variance of the fundamental sampling error is reviewed. Practical situations where the generalized model potentially leads to more accurate variance estimates are identified as: clustering of particles, differences in densities or sizes of the particles or repulsive int... |
Title: A Discourse-based Approach in Text-based Machine Translation |
Abstract: This paper presents a theoretical research based approach to ellipsis resolution in machine translation. The formula of discourse is applied in order to resolve ellipses. The validity of the discourse formula is analyzed by applying it to the real world text, i.e., newspaper fragments. The source text is conv... |
Title: Resolution of Unidentified Words in Machine Translation |
Abstract: This paper presents a mechanism of resolving unidentified lexical units in Text-based Machine Translation (TBMT). In a Machine Translation (MT) system it is unlikely to have a complete lexicon and hence there is intense need of a new mechanism to handle the problem of unidentified words. These unknown words c... |
Title: Manipulating Tournaments in Cup and Round Robin Competitions |
Abstract: In sports competitions, teams can manipulate the result by, for instance, throwing games. We show that we can decide how to manipulate round robin and cup competitions, two of the most popular types of sporting competitions in polynomial time. In addition, we show that finding the minimal number of games that... |
Title: Industrial-Strength Formally Certified SAT Solving |
Abstract: Boolean Satisfiability (SAT) solvers are now routinely used in the verification of large industrial problems. However, their application in safety-critical domains such as the railways, avionics, and automotive industries requires some form of assurance for the results, as the solvers can (and sometimes do) h... |
Title: Multi-Objective Optimisation Method for Posture Prediction and Analysis with Consideration of Fatigue Effect and its Application Case |
Abstract: Automation technique has been widely used in manufacturing industry, but there are still manual handling operations required in assembly and maintenance work in industry. Inappropriate posture and physical fatigue might result in musculoskeletal disorders (MSDs) in such physical jobs. In ergonomics and occupa... |
Title: Simulation-based model selection for dynamical systems in systems and population biology |
Abstract: Computer simulations have become an important tool across the biomedical sciences and beyond. For many important problems several different models or hypotheses exist and choosing which one best describes reality or observed data is not straightforward. We therefore require suitable statistical tools that all... |
Title: A Dynamic Vulnerability Map to Assess the Risk of Road Network Traffic Utilization |
Abstract: Le Havre agglomeration (CODAH) includes 16 establishments classified Seveso with high threshold. In the literature, we construct vulnerability maps to help decision makers assess the risk. Such approaches remain static and do take into account the population displacement in the estimation of the vulnerability... |
Title: Different goals in multiscale simulations and how to reach them |
Abstract: In this paper we sum up our works on multiscale programs, mainly simulations. We first start with describing what multiscaling is about, how it helps perceiving signal from a background noise in a ?ow of data for example, for a direct perception by a user or for a further use by another program. We then give ... |
Title: Benchmarking Historical Corporate Performance |
Abstract: This paper uses Bayesian tree models for statistical benchmarking in data sets with awkward marginals and complicated dependence structures. The method is applied to a very large database on corporate performance over the last four decades. The results of this study provide a formal basis for making cross-pee... |
Title: Standards for Language Resources |
Abstract: The goal of this paper is two-fold: to present an abstract data model for linguistic annotations and its implementation using XML, RDF and related standards; and to outline the work of a newly formed committee of the International Standards Organization (ISO), ISO/TC 37/SC 4 Language Resource Management, whic... |
Title: On Bayesian Curve Fitting Via Auxiliary Variables |
Abstract: In this article we revisit the auxiliary variable method introduced in Smith and kohn (1996) for the fitting of P-th order spline regression models with an unknown number of knot points. We introduce modifications which allow the location of knot points to be random, and we further consider an extension of th... |
Title: Regression on a Graph |
Abstract: The `Signal plus Noise' model for nonparametric regression can be extended to the case of observations taken at the vertices of a graph. This model includes many familiar regression problems. This article discusses the use of the edges of a graph to measure roughness in penalized regression. Distance between ... |
Title: Active Learning for Mention Detection: A Comparison of Sentence Selection Strategies |
Abstract: We propose and compare various sentence selection strategies for active learning for the task of detecting mentions of entities. The best strategy employs the sum of confidences of two statistical classifiers trained on different views of the data. Our experimental results show that, compared to the random se... |
Title: Statistical applications of the multivariate skew-normal distribution |
Abstract: Azzalini & Dalla Valle (1996) have recently discussed the multivariate skew-normal distribution which extends the class of normal distributions by the addition of a shape parameter. The first part of the present paper examines further probabilistic properties of the distribution, with special emphasis on aspe... |
Title: A New Look at the Classical Entropy of Written English |
Abstract: A simple method for finding the entropy and redundancy of a reasonable long sample of English text by direct computer processing and from first principles according to Shannon theory is presented. As an example, results on the entropy of the English language have been obtained based on a total of 20.3 million... |
Title: Distributions generated by perturbation of symmetry with emphasis on a multivariate skew $t$ distribution |
Abstract: A fairly general procedure is studied to perturbate a multivariate density satisfying a weak form of multivariate symmetry, and to generate a whole set of non-symmetric densities. The approach is general enough to encompass a number of recent proposals in the literature, variously related to the skew normal d... |
Title: High dimensional sparse covariance estimation via directed acyclic graphs |
Abstract: We present a graph-based technique for estimating sparse covariance matrices and their inverses from high-dimensional data. The method is based on learning a directed acyclic graph (DAG) and estimating parameters of a multivariate Gaussian distribution based on a DAG. For inferring the underlying DAG we use t... |
Title: Analytical Determination of Fractal Structure in Stochastic Time Series |
Abstract: Current methods for determining whether a time series exhibits fractal structure (FS) rely on subjective assessments on estimators of the Hurst exponent (H). Here, I introduce the Bayesian Assessment of Scaling, an analytical framework for drawing objective and accurate inferences on the FS of time series. Th... |
Title: How Creative Should Creators Be To Optimize the Evolution of Ideas? A Computational Model |
Abstract: There are both benefits and drawbacks to creativity. In a social group it is not necessary for all members to be creative to benefit from creativity; some merely imitate or enjoy the fruits of others' creative efforts. What proportion should be creative? This paper contains a very preliminary investigation of... |
Title: Emotion: Appraisal-coping model for the "Cascades" problem |
Abstract: Modelling emotion has become a challenge nowadays. Therefore, several models have been produced in order to express human emotional activity. However, only a few of them are currently able to express the close relationship existing between emotion and cognition. An appraisal-coping model is presented here, wi... |
Title: Emotion : mod\`ele d'appraisal-coping pour le probl\`eme des Cascades |
Abstract: Modeling emotion has become a challenge nowadays. Therefore, several models have been produced in order to express human emotional activity. However, only a few of them are currently able to express the close relationship existing between emotion and cognition. An appraisal-coping model is presented here, wit... |
Title: Local statistical modeling by cluster-weighted |
Abstract: We investigate statistical properties of Cluster-Weighted Modeling, which is a framework for supervised learning originally developed in order to recreate a digital violin with traditional inputs and realistic sound. The analysis is carried out in comparison with Finite Mixtures of Regression models. Based on... |
Title: Proceedings Fifth Workshop on Developments in Computational Models--Computational Models From Nature |
Abstract: The special theme of DCM 2009, co-located with ICALP 2009, concerned Computational Models From Nature, with a particular emphasis on computational models derived from physics and biology. The intention was to bring together different approaches - in a community with a strong foundational background as proffer... |
Title: Neural Networks for Dynamic Shortest Path Routing Problems - A Survey |
Abstract: This paper reviews the overview of the dynamic shortest path routing problem and the various neural networks to solve it. Different shortest path optimization problems can be solved by using various neural networks algorithms. The routing in packet switched multi-hop networks can be described as a classical c... |
Title: A Hierarchical Bayesian Model for Frame Representation |
Abstract: In many signal processing problems, it may be fruitful to represent the signal under study in a frame. If a probabilistic approach is adopted, it becomes then necessary to estimate the hyper-parameters characterizing the probability distribution of the frame coefficients. This problem is difficult since in ge... |
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