text stringlengths 0 4.09k |
|---|
Abstract: In this paper, the framework of kernel machines with two layers is introduced, generalizing classical kernel methods. The new learning methodology provide a formal connection between computational architectures with multiple layers and the theme of kernel learning in standard regularization methods. First, a ... |
Title: Scalable Bayesian reduced-order models for high-dimensional multiscale dynamical systems |
Abstract: While existing mathematical descriptions can accurately account for phenomena at microscopic scales (e.g. molecular dynamics), these are often high-dimensional, stochastic and their applicability over macroscopic time scales of physical interest is computationally infeasible or impractical. In complex systems... |
Title: Adaptive Gibbs samplers |
Abstract: We consider various versions of adaptive Gibbs and Metropolis within-Gibbs samplers, which update their selection probabilities (and perhaps also their proposal distributions) on the fly during a run, by learning as they go in an attempt to optimise the algorithm. We present a cautionary example of how even a... |
Title: A Monte Carlo Algorithm for Universally Optimal Bayesian Sequence Prediction and Planning |
Abstract: The aim of this work is to address the question of whether we can in principle design rational decision-making agents or artificial intelligences embedded in computable physics such that their decisions are optimal in reasonable mathematical senses. Recent developments in rare event probability estimation, re... |
Title: Introducing Monte Carlo Methods with R Solutions to Odd-Numbered Exercises |
Abstract: This is the solution manual to the odd-numbered exercises in our book "Introducing Monte Carlo Methods with R", published by Springer Verlag on December 10, 2009, and made freely available to everyone. |
Title: Asymptotic Learning Curve and Renormalizable Condition in Statistical Learning Theory |
Abstract: Bayes statistics and statistical physics have the common mathematical structure, where the log likelihood function corresponds to the random Hamiltonian. Recently, it was discovered that the asymptotic learning curves in Bayes estimation are subject to a universal law, even if the log likelihood function can ... |
Title: Comment on "Harold Jeffreys's Theory of Probability Revisited" |
Abstract: Comment on "Harold Jeffreys's Theory of Probability Revisited" [arXiv:0804.3173] |
Title: Bayes, Jeffreys, Prior Distributions and the Philosophy of Statistics |
Abstract: Discussion of "Harold Jeffreys's Theory of Probability revisited," by Christian Robert, Nicolas Chopin, and Judith Rousseau, for Statistical Science [arXiv:0804.3173] |
Title: Comment: The Importance of Jeffreys's Legacy |
Abstract: Theory of Probability is distinguished by several high-level philosophical attitudes, some stressed by Jeffreys, some implicit. By reviewing these we may recognize the importance in this work in the historical development of statistics. [arXiv:0804.3173] |
Title: Comment on "Harold Jeffreys's Theory of Probability Revisited" |
Abstract: Comment on "Harold Jeffreys's Theory of Probability Revisited" [arXiv:0804.3173] |
Title: Comment on "Harold Jeffreys's Theory of Probability Revisited" |
Abstract: Comment on "Harold Jeffreys's Theory of Probability Revisited" [arXiv:0804.3173] |
Title: A Multivariate Variance Components Model for Analysis of Covariance in Designed Experiments |
Abstract: Traditional methods for covariate adjustment of treatment means in designed experiments are inherently conditional on the observed covariate values. In order to develop a coherent general methodology for analysis of covariance, we propose a multivariate variance components model for the joint distribution of ... |
Title: Comment on "Harold Jeffreys's Theory of Probability Revisited" |
Abstract: Comment on "Harold Jeffreys's Theory of Probability Revisited" [arXiv:0804.3173] |
Title: Feature Extraction for Universal Hypothesis Testing via Rank-constrained Optimization |
Abstract: This paper concerns the construction of tests for universal hypothesis testing problems, in which the alternate hypothesis is poorly modeled and the observation space is large. The mismatched universal test is a feature-based technique for this purpose. In prior work it is shown that its finite-observation pe... |
Title: Increasing stability and interpretability of gene expression signatures |
Abstract: Motivation : Molecular signatures for diagnosis or prognosis estimated from large-scale gene expression data often lack robustness and stability, rendering their biological interpretation challenging. Increasing the signature's interpretability and stability across perturbations of a given dataset and, if pos... |
Title: An Immuno-Inspired Approach to Misbehavior Detection in Ad Hoc Wireless Networks |
Abstract: We propose and evaluate an immuno-inspired approach to misbehavior detection in ad hoc wireless networks. Node misbehavior can be the result of an intrusion, or a software or hardware failure. Our approach is motivated by co-stimulatory signals present in the Biological immune system. The results show that co... |
Title: Bayesian Thought in Early Modern Detective Stories: Monsieur Lecoq, C. Auguste Dupin and Sherlock Holmes |
Abstract: This paper reviews the maxims used by three early modern fictional detectives: Monsieur Lecoq, C. Auguste Dupin and Sherlock Holmes. It find similarities between these maxims and Bayesian thought. Poe's Dupin uses ideas very similar to Bayesian game theory. Sherlock Holmes' statements also show thought patter... |
Title: A Conversation with Shayle R. Searle |
Abstract: Born in New Zealand, Shayle Robert Searle earned a bachelor's degree (1949) and a master's degree (1950) from Victoria University, Wellington, New Zealand. After working for an actuary, Searle went to Cambridge University where he earned a Diploma in mathematical statistics in 1953. Searle won a Fulbright tra... |
Title: Bayesian inference for queueing networks and modeling of internet services |
Abstract: Modern Internet services, such as those at Google, Yahoo!, and Amazon, handle billions of requests per day on clusters of thousands of computers. Because these services operate under strict performance requirements, a statistical understanding of their performance is of great practical interest. Such services... |
Title: The dynamics of message passing on dense graphs, with applications to compressed sensing |
Abstract: Approximate message passing algorithms proved to be extremely effective in reconstructing sparse signals from a small number of incoherent linear measurements. Extensive numerical experiments further showed that their dynamics is accurately tracked by a simple one-dimensional iteration termed state evolution.... |
Title: Role of Interestingness Measures in CAR Rule Ordering for Associative Classifier: An Empirical Approach |
Abstract: Associative Classifier is a novel technique which is the integration of Association Rule Mining and Classification. The difficult task in building Associative Classifier model is the selection of relevant rules from a large number of class association rules (CARs). A very popular method of ordering rules for ... |
Title: Features Based Text Similarity Detection |
Abstract: As the Internet help us cross cultural border by providing different information, plagiarism issue is bound to arise. As a result, plagiarism detection becomes more demanding in overcoming this issue. Different plagiarism detection tools have been developed based on various detection techniques. Nowadays, fin... |
Title: 3D Skull Recognition Using 3D Matching Technique |
Abstract: Biometrics has become a "hot" area. Governments are funding research programs focused on biometrics. In this paper the problem of person recognition and verification based on a different biometric application has been addressed. The system is based on the 3DSkull recognition using 3D matching technique, in fa... |
Title: Hybrid Medical Image Classification Using Association Rule Mining with Decision Tree Algorithm |
Abstract: The main focus of image mining in the proposed method is concerned with the classification of brain tumor in the CT scan brain images. The major steps involved in the system are: pre-processing, feature extraction, association rule mining and hybrid classifier. The pre-processing step has been done using the ... |
Title: Gradient Based Seeded Region Grow method for CT Angiographic Image Segmentation |
Abstract: Segmentation of medical images using seeded region growing technique is increasingly becoming a popular method because of its ability to involve high-level knowledge of anatomical structures in seed selection process. Region based segmentation of medical images are widely used in varied clinical applications ... |
Title: Estimation in functional regression for general exponential families |
Abstract: This paper studies a class of exponential family models whose canonical parameters are specified as linear functionals of an unknown infinite-dimensional slope function. The optimal minimax rates of convergence for slope function estimation are established. The estimators that achieve the optimal rates are co... |
Title: The effect of discrete vs. continuous-valued ratings on reputation and ranking systems |
Abstract: When users rate objects, a sophisticated algorithm that takes into account ability or reputation may produce a fairer or more accurate aggregation of ratings than the straightforward arithmetic average. Recently a number of authors have proposed different co-determination algorithms where estimates of user an... |
Title: Strict Monotonicity and Convergence Rate of Titterington's Algorithm for Computing D-optimal Designs |
Abstract: We study a class of multiplicative algorithms introduced by Silvey et al. (1978) for computing D-optimal designs. Strict monotonicity is established for a variant considered by Titterington (1978). A formula for the rate of convergence is also derived. This is used to explain why modifications considered by T... |
Title: Robustness and accuracy of methods for high dimensional data analysis based on Student's t statistic |
Abstract: Student's $t$ statistic is finding applications today that were never envisaged when it was introduced more than a century ago. Many of these applications rely on properties, for example robustness against heavy tailed sampling distributions, that were not explicitly considered until relatively recently. In t... |
Title: Non-Gaussian Quasi Maximum Likelihood Estimation of GARCH Models |
Abstract: The non-Gaussian quasi maximum likelihood estimator is frequently used in GARCH models with intension to improve the efficiency of the GARCH parameters. However, unless the quasi-likelihood happens to be the true one, non-Gaussian QMLE methods suffers inconsistency even if shape parameters in the quasi-likeli... |
Title: Robust and Trend-following Kalman Smoothers using Student's t |
Abstract: We propose two nonlinear Kalman smoothers that rely on Student's t distributions. The T-Robust smoother finds the maximum a posteriori likelihood (MAP) solution for Gaussian process noise and Student's t observation noise, and is extremely robust against outliers, outperforming the recently proposed l1-Laplac... |
Title: Classifying Network Data with Deep Kernel Machines |
Abstract: Inspired by a growing interest in analyzing network data, we study the problem of node classification on graphs, focusing on approaches based on kernel machines. Conventionally, kernel machines are linear classifiers in the implicit feature space. We argue that linear classification in the feature space of ke... |
Title: Relative Age Effect in Elite Sports: Methodological Bias or Real Discrimination? |
Abstract: Sport sciences researchers talk about a relative age effect when they observe a biased distribution of elite athletes' birthdates, with an over-representation of those born at the beginning of the competitive year and an under-representation of those born at the end. Using the whole sample of the French male ... |
Title: Grouping Priors and the Bayesian Elastic Net |
Abstract: In the literature surrounding Bayesian penalized regression, the two primary choices of prior distribution on the regression coefficients are zero-mean Gaussian and Laplace. While both have been compared numerically and theoretically, there remains little guidance on which to use in real-life situations. We p... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.