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Title: Component Selection in the Additive Regression Model |
Abstract: Similar to variable selection in the linear regression model, selecting significant components in the popular additive regression model is of great interest. However, such components are unknown smooth functions of independent variables, which are unobservable. As such, some approximation is needed. In this p... |
Title: A Fast Statistical Method for Multilevel Thresholding in Wavelet Domain |
Abstract: An algorithm is proposed for the segmentation of image into multiple levels using mean and standard deviation in the wavelet domain. The procedure provides for variable size segmentation with bigger block size around the mean, and having smaller blocks at the ends of histogram plot of each horizontal, vertica... |
Title: A Framework for Real-Time Face and Facial Feature Tracking using Optical Flow Pre-estimation and Template Tracking |
Abstract: This work presents a framework for tracking head movements and capturing the movements of the mouth and both the eyebrows in real-time. We present a head tracker which is a combination of a optical flow and a template based tracker. The estimation of the optical flow head tracker is used as starting point for... |
Title: Generalised Wishart Processes |
Abstract: We introduce a stochastic process with Wishart marginals: the generalised Wishart process (GWP). It is a collection of positive semi-definite random matrices indexed by any arbitrary dependent variable. We use it to model dynamic (e.g. time varying) covariance matrices. Unlike existing models, it can capture ... |
Title: Binary and nonbinary description of hypointensity in human brain MR images |
Abstract: Accumulating evidence has shown that iron is involved in the mechanism underlying many neurodegenerative diseases, such as Alzheimer's disease, Parkinson's disease and Huntington's disease. Abnormal (higher) iron accumulation has been detected in the brains of most neurodegenerative patients, especially in th... |
Title: Use of Python and Phoenix-M Interface in Robotics |
Abstract: In this paper I will show how to use Python programming with a computer interface such as Phoenix-M 1 to drive simple robots. In my quest towards Artificial Intelligence(AI) I am experimenting with a lot of different possibilities in Robotics. This one will try to mimic the working of a simple insect's nervou... |
Title: Conditional information and definition of neighbor in categorical random fields |
Abstract: We show that the definition of neighbor in Markov random fields as defined by Besag (1974) when the joint distribution of the sites is not positive is not well-defined. In a random field with finite number of sites we study the conditions under which giving the value at extra sites will change the belief of a... |
Title: Measuring support for a hypothesis about a random parameter without estimating its unknown prior |
Abstract: For frequentist settings in which parameter randomness represents variability rather than uncertainty, the ideal measure of the support for one hypothesis over another is the difference in the posterior and prior log odds. For situations in which the prior distribution cannot be accurately estimated, that ide... |
Title: Concrete Sentence Spaces for Compositional Distributional Models of Meaning |
Abstract: Coecke, Sadrzadeh, and Clark (arXiv:1003.4394v1 [cs.CL]) developed a compositional model of meaning for distributional semantics, in which each word in a sentence has a meaning vector and the distributional meaning of the sentence is a function of the tensor products of the word vectors. Abstractly speaking, ... |
Title: Bistatic SAR ATR |
Abstract: With the present revival of interest in bistatic radar systems, research in that area has gained momentum. Given some of the strategic advantages for a bistatic configuration, and tech- nological advances in the past few years, large-scale implementation of the bistatic systems is a scope for the near future.... |
Title: Generation of SAR Image for Real-life Objects using General Purpose EM Simulators |
Abstract: In the applications related to airborne radars, simulation has always played an important role. This is mainly because of the two fold reason of the unavailability of desired data and the difficulty associated with the collection of data under controlled environment. A simple example will be regarding the col... |
Title: Combining Neural Networks for Skin Detection |
Abstract: Two types of combining strategies were evaluated namely combining skin features and combining skin classifiers. Several combining rules were applied where the outputs of the skin classifiers are combined using binary operators such as the AND and the OR operators, "Voting", "Sum of Weights" and a new neural n... |
Title: MCMC Using Ensembles of States for Problems with Fast and Slow Variables such as Gaussian Process Regression |
Abstract: I introduce a Markov chain Monte Carlo (MCMC) scheme in which sampling from a distribution with density pi(x) is done using updates operating on an "ensemble" of states. The current state x is first stochastically mapped to an ensemble, x^(1),...,x^(K). This ensemble is then updated using MCMC updates that le... |
Title: Bayesian inference for a class of latent Markov models for categorical longitudinal data |
Abstract: We propose a Bayesian inference approach for a class of latent Markov models. These models are widely used for the analysis of longitudinal categorical data, when the interest is in studying the evolution of an individual unobservable characteristic. We consider, in particular, the basic latent Markov, which ... |
Title: The Local Optimality of Reinforcement Learning by Value Gradients, and its Relationship to Policy Gradient Learning |
Abstract: In this theoretical paper we are concerned with the problem of learning a value function by a smooth general function approximator, to solve a deterministic episodic control problem in a large continuous state space. It is shown that learning the gradient of the value-function at every point along a trajector... |
Title: Sparse recovery with unknown variance: a LASSO-type approach |
Abstract: We address the issue of estimating the regression vector $\beta$ in the generic $s$-sparse linear model $y = X\beta+z$, with $\beta\in\R^p$, $y\in\R^n$, $z\sim\mathcal N(0,\sg^2 I)$ and $p> n$ when the variance $\sg^2$ is unknown. We study two LASSO-type methods that jointly estimate $\beta$ and the variance.... |
Title: Segmentation of Camera Captured Business Card Images for Mobile Devices |
Abstract: Due to huge deformation in the camera captured images, variety in nature of the business cards and the computational constraints of the mobile devices, design of an efficient Business Card Reader (BCR) is challenging to the researchers. Extraction of text regions and segmenting them into characters is one of ... |
Title: Good Friends, Bad News - Affect and Virality in Twitter |
Abstract: The link between affect, defined as the capacity for sentimental arousal on the part of a message, and virality, defined as the probability that it be sent along, is of significant theoretical and practical importance, e.g. for viral marketing. A quantitative study of emailing of articles from the NY Times fi... |
Title: Sparse Partitioning: Nonlinear regression with binary or tertiary predictors, with application to association studies |
Abstract: This paper presents Sparse Partitioning, a Bayesian method for identifying predictors that either individually or in combination with others affect a response variable. The method is designed for regression problems involving binary or tertiary predictors and allows the number of predictors to exceed the size... |
Title: Autoregressive Kernels For Time Series |
Abstract: We propose in this work a new family of kernels for variable-length time series. Our work builds upon the vector autoregressive (VAR) model for multivariate stochastic processes: given a multivariate time series x, we consider the likelihood function p_\theta(x) of different parameters \theta in the VAR model... |
Title: Nonparametric Additive Model-assisted Estimation for Survey Data |
Abstract: An additive model-assisted nonparametric method is investigated to estimate the finite population totals of massive survey data with the aid of auxiliary information. A class of estimators is proposed to improve the precision of the well known Horvitz-Thompson estimators by combining the spline and local poly... |
Title: To Explain or to Predict? |
Abstract: Statistical modeling is a powerful tool for developing and testing theories by way of causal explanation, prediction, and description. In many disciplines there is near-exclusive use of statistical modeling for causal explanation and the assumption that models with high explanatory power are inherently of hig... |
Title: On the Sample Information About Parameter and Prediction |
Abstract: The Bayesian measure of sample information about the parameter, known as Lindley's measure, is widely used in various problems such as developing prior distributions, models for the likelihood functions and optimal designs. The predictive information is defined similarly and used for model selection and optim... |
Title: Graphical Models for Inference Under Outcome-Dependent Sampling |
Abstract: We consider situations where data have been collected such that the sampling depends on the outcome of interest and possibly further covariates, as for instance in case-control studies. Graphical models represent assumptions about the conditional independencies among the variables. By including a node for the... |
Title: Laplace Approximated EM Microarray Analysis: An Empirical Bayes Approach for Comparative Microarray Experiments |
Abstract: A two-groups mixed-effects model for the comparison of (normalized) microarray data from two treatment groups is considered. Most competing parametric methods that have appeared in the literature are obtained as special cases or by minor modification of the proposed model. Approximate maximum likelihood fitti... |
Title: A Conversation with George C. Tiao |
Abstract: George C. Tiao was born in London in 1933. After graduating with a B.A. in Economics from National Taiwan University in 1955 he went to the US to obtain an M.B.A from New York University in 1958 and a Ph.D. in Economics from the University of Wisconsin, Madison in 1962. From 1962 to 1982 he was Assistant, Ass... |
Title: Bivariate Uniform Deconvolution |
Abstract: We construct a density estimator in the bivariate uniform deconvolution model. For this model we derive four inversion formulas to express the bivariate density that we want to estimate in terms of the bivariate density of the observations. By substituting a kernel density estimator of the density of the obse... |
Title: Approximate Bayesian Computational methods |
Abstract: Also known as likelihood-free methods, approximate Bayesian computational (ABC) methods have appeared in the past ten years as the most satisfactory approach to untractable likelihood problems, first in genetics then in a broader spectrum of applications. However, these methods suffer to some degree from cali... |
Title: A Family of Generalized Linear Models for Repeated Measures with Normal and Conjugate Random Effects |
Abstract: Non-Gaussian outcomes are often modeled using members of the so-called exponential family. Notorious members are the Bernoulli model for binary data, leading to logistic regression, and the Poisson model for count data, leading to Poisson regression. Two of the main reasons for extending this family are (1) t... |
Title: Change-point in stochastic design regression and the bootstrap |
Abstract: In this paper we study the consistency of different bootstrap procedures for constructing confidence intervals (CIs) for the unique jump discontinuity (change-point) in an otherwise smooth regression function in a stochastic design setting. This problem exhibits nonstandard asymptotics and we argue that the s... |
Title: Sparsity regret bounds for individual sequences in online linear regression |
Abstract: We consider the problem of online linear regression on arbitrary deterministic sequences when the ambient dimension d can be much larger than the number of time rounds T. We introduce the notion of sparsity regret bound, which is a deterministic online counterpart of recent risk bounds derived in the stochast... |
Title: Marginal Likelihood Computation via Arrogance Sampling |
Abstract: This paper describes a method for estimating the marginal likelihood or Bayes factors of Bayesian models using non-parametric importance sampling ("arrogance sampling"). This method can also be used to compute the normalizing constant of probability distributions. Because the required inputs are samples from ... |
Title: Bayesian Analysis of Loss Ratios Using the Reversible Jump Algorithm |
Abstract: In this paper we consider the problem of model choice for a set of insurance loss ratios. We use a reversible jump algorithm for our model discrimination and show how the vanilla reversible jump algorithm can be improved on using recent methodological advances in reversible jump computation. |
Title: Extending Bron Kerbosch for Solving the Maximum Weight Clique Problem |
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