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Results 1 - 10
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10,609
Hierarchical Modelling and Analysis for Spatial Data. Chapman and Hall/CRC,
, 2004
"... Abstract Often, there are two streams in statistical research -one developed by practitioners and other by main stream statisticians. Development of geostatistics is a very good example where pioneering work under realistic assumptions came from mining engineers whereas it is only now that statisti ..."
Abstract
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Cited by 442 (45 self)
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be estimated by ML. Furthermore, ML provides an uncertainty of the estimated parameters what may be used to decide if point estimates are sufficiently accurate or if interval estimation (giving a region of suitable parameters) is more adequate. We discuss various practical and theoretical problems with MLE
Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments
- STATISTICA SINICA
, 2002
"... DNA microarrays are a new and promising biotechnology whichallows the monitoring of expression levels in cells for thousands of genes simultaneously. The present paper describes statistical methods for the identification of differentially expressed genes in replicated cDNA microarray experiments. A ..."
Abstract
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Cited by 438 (12 self)
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. Although it is not the main focus of the paper, new methods for the important pre-processing steps of image analysis and normalization are proposed. Given suitably normalized data, the biological question of differential expression is restated as a problem in multiple hypothesis testing: the simultaneous
Policy gradient methods for reinforcement learning with function approximation.
- In NIPS,
, 1999
"... Abstract Function approximation is essential to reinforcement learning, but the standard approach of approximating a value function and determining a policy from it has so far proven theoretically intractable. In this paper we explore an alternative approach in which the policy is explicitly repres ..."
Abstract
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Cited by 439 (20 self)
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that the gradient can be written in a form suitable for estimation from experience aided by an approximate action-value or advantage function. Using this result, we prove for the first time that a version of policy iteration with arbitrary differentiable function approximation is convergent to a locally optimal
Wavelet-based statistical signal processing using hidden Markov models
- IEEE TRANSACTIONS ON SIGNAL PROCESSING
, 1998
"... Wavelet-based statistical signal processing techniques such as denoising and detection typically model the wavelet coefficients as independent or jointly Gaussian. These models are unrealistic for many real-world signals. In this paper, we develop a new framework for statistical signal processing b ..."
Abstract
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Cited by 415 (50 self)
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, probabilistic signal models. Efficient expectation maximization algorithms are developed for fitting the HMM’s to observational signal data. The new framework is suitable for a wide range of applications, including signal estimation, detection, classification, prediction, and even synthesis. To demonstrate
Complex wavelets for shift invariant analysis and filtering of signals
- J. Applied and Computational Harmonic Analysis
, 2001
"... This paper describes a form of discrete wavelet transform, which generates complex coefficients by using a dual tree of wavelet filters to obtain their real and imaginary parts. This introduces limited redundancy (2m: 1 for m-dimensional signals) and allows the transform to provide approximate shift ..."
Abstract
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Cited by 384 (40 self)
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to be shift invariant and describe how to estimate the accuracy of this approximation and design suitable filters to achieve this. We discuss two different variants of the new transform, based on odd/even and quarter-sample shift (Q-shift) filters, respectively. We then describe briefly how the dual tree may
Using mutual information for selecting features in supervised neural net learning
- IEEE TRANSACTIONS ON NEURAL NETWORKS
, 1994
"... This paper investigates the application of the mutual infor“ criterion to evaluate a set of candidate features and to select an informative subset to be used as input data for a neural network classifier. Because the mutual information measures arbitrary dependencies between random variables, it is ..."
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Cited by 358 (1 self)
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, it is suitable for assessing the “information content ” of features in complex classification tasks, where methods bases on linear relations (like the correlation) are prone to mistakes. The fact that the mutual information is independent of the coordinates chosen permits a robust estimation. Nonetheless
Realistic Modeling for Facial Animation
, 1995
"... A major unsolved problem in computer graphics is the construction and animation of realistic human facial models. Traditionally, facial models have been built painstakingly by manual digitization and animated by ad hoc parametrically controlled facial mesh deformations or kinematic approximation of ..."
Abstract
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Cited by 362 (14 self)
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suitable for animation. In this paper, we present a methodology for automating this challenging task. Starting with a structured facial mesh, we develop algorithms that automatically construct functional models of the heads of human subjects from laser-scanned range and reflectance data. These algorithms
Article Uncertainty in Various Habitat Suitability Models and Its Impact on Habitat Suitability Estimates for Fish
"... www.mdpi.com/journal/water ..."
Adaptive Duplicate Detection Using Learnable String Similarity Measures
- In Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2003
, 2003
"... The problem of identifying approximately duplicate records in databases is an essential step for data cleaning and data integration processes. Most existing approaches have relied on generic or manually tuned distance metrics for estimating the similarity of potential duplicates. In this paper, we p ..."
Abstract
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Cited by 344 (14 self)
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The problem of identifying approximately duplicate records in databases is an essential step for data cleaning and data integration processes. Most existing approaches have relied on generic or manually tuned distance metrics for estimating the similarity of potential duplicates. In this paper, we
Nonlinear spatial normalization using basis functions
- Human Brain Mapping
, 1999
"... Abstract: We describe a comprehensive framework for performing rapid and automatic nonlabel-based nonlinear spatial normalizations. The approach adopted minimizes the residual squared difference between an image and a template of the same modality. In order to reduce the number of parameters to be f ..."
Abstract
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Cited by 329 (19 self)
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the smoothness of the transformation using a maximum a posteriori (MAP) approach. Most MAP approaches assume that the variance associated with each voxel is already known and that there is no covariance between neighboring voxels. The approach described here attempts to estimate this variance from the data
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