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381
Beyond tracking: modelling activity and understanding behaviour
 International Journal of Computer Vision
, 2006
"... In this work, we present a unified bottomup and topdown automatic model selection based approach for modelling complex activities of multiple objects in cluttered scenes. An activity of multiple objects is represented based on discrete scene events and their behaviours are modelled by reasoning ab ..."
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Cited by 55 (13 self)
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In this work, we present a unified bottomup and topdown automatic model selection based approach for modelling complex activities of multiple objects in cluttered scenes. An activity of multiple objects is represented based on discrete scene events and their behaviours are modelled by reasoning about the temporal and causal correlations among different events. This is significantly different from the majority of the existing techniques that are centred on object tracking followed by trajectory matching. In our approach, objectindependent events are detected and classified by unsupervised clustering using ExpectationMaximisation (EM) and classified using automatic model selection based on Schwarz’s Bayesian Information Criterion (BIC). Dynamic Probabilistic Networks (DPNs) are formulated for modelling the temporal and causal correlations among discrete events for robust and holistic scenelevel behaviour interpretation. In particular, we developed a Dynamically MultiLinked Hidden Markov Model (DMLHMM) based on the discovery of salient dynamic interlinks among multiple temporal processes corresponding to multiple event classes. A DMLHMM is built using BIC based factorisation resulting in its topology being intrinsically determined by the underlying causality and temporal order among events. Extensive experiments are conducted on modelling activities captured in different indoor and
Bayesian model averaging
 STAT.SCI
, 1999
"... Standard statistical practice ignores model uncertainty. Data analysts typically select a model from some class of models and then proceed as if the selected model had generated the data. This approach ignores the uncertainty in model selection, leading to overcon dent inferences and decisions tha ..."
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Cited by 49 (1 self)
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Standard statistical practice ignores model uncertainty. Data analysts typically select a model from some class of models and then proceed as if the selected model had generated the data. This approach ignores the uncertainty in model selection, leading to overcon dent inferences and decisions that are more risky than one thinks they are. Bayesian model averaging (BMA) provides a coherent mechanism for accounting for this model uncertainty. Several methods for implementing BMA haverecently emerged. We discuss these methods and present anumber of examples. In these examples, BMA provides improved outofsample predictive performance. We also provide a catalogue of
The Great Equalizer? Consumer Choice Behavior at Internet Shopbots
 SLOAN SCHOOL OF MANAGEMENT, MIT
, 2000
"... Our research empirically analyzes consumer behavior at Internet shopbots — sites that allow consumers to make “oneclick ” price comparisons for product offerings from multiple retailers. By allowing researchers to observe exactly what information the consumer is shown and their search behavior in r ..."
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Cited by 36 (0 self)
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Our research empirically analyzes consumer behavior at Internet shopbots — sites that allow consumers to make “oneclick ” price comparisons for product offerings from multiple retailers. By allowing researchers to observe exactly what information the consumer is shown and their search behavior in response to this information, shopbot data has unique strengths for analyzing consumer behavior. Furthermore, the method in which the data is displayed to consumers lends itself to a utilitybased evaluation process, consistent with econometric analysis techniques. While price is an important determinant of customer choice, we find that, even among shopbot consumers, branded retailers and retailers a consumer visited previously hold significant price advantages in headtohead price comparisons. Further, customers are very sensitive to how the total price is allocated among the item price, the shipping cost, and tax, and are also quite sensitive to the ordinal ranking of retailer offerings with respect to price. We also find that consumers use brand as a proxy for a retailer’s credibility with regard to noncontractible aspects of the product bundle such as shipping time. In each case our models accurately predict consumer behavior out of sample, suggesting
Unsupervised Audio Stream Segmentation And Clustering Via The Bayesian Information Criterion
 in Proc. ISCLP 2000
, 2000
"... In this paper, we propose an ecient approach for unsupervised audio stream segmentation and clustering via the Bayesian Information Criterion (BIC). The proposed method extends an earlier formulation by Chen and Gopalakrishnan[1]. In our segmentation formulation, Hotelling's $T^2$statistic is ..."
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Cited by 35 (2 self)
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In this paper, we propose an ecient approach for unsupervised audio stream segmentation and clustering via the Bayesian Information Criterion (BIC). The proposed method extends an earlier formulation by Chen and Gopalakrishnan[1]. In our segmentation formulation, Hotelling's $T^2$statistic is used to preselect candidate segmentation boundaries followed by BIC to make the segmentation decision. Our experiments show that we can improve the final algorithm speed by an order of 100 compared to that in [1] while achieving a 7% reduced miss rate at the expense of a 6% increase in false alarm rate using DARPA Hub4 1997 evaluation data. In the clustering stage, Gaussian Mixture Models are used for gender labeling prior to hierarchical BICbased clustering within the gender class. Our cluster experiment show that we can achieve a cluster purity of 99.3%.
Statistical Themes and Lessons for Data Mining
, 1997
"... Data mining is on the interface of Computer Science and Statistics, utilizing advances in both disciplines to make progress in extracting information from large databases. It is an emerging field that has attracted much attention in a very short period of time. This article highlights some statist ..."
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Cited by 33 (3 self)
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Data mining is on the interface of Computer Science and Statistics, utilizing advances in both disciplines to make progress in extracting information from large databases. It is an emerging field that has attracted much attention in a very short period of time. This article highlights some statistical themes and lessons that are directly relevant to data mining and attempts to identify opportunities where close cooperation between the statistical and computational communities might reasonably provide synergy for further progress in data analysis.
Small Sample Statistics for Classification Error Rates I: Error Rate Measurements
 Dept. of Inf. and Comp. Sci
, 1996
"... Several methods (independent subsamples, leaveoneout, crossvalidation, and bootstrapping) have been proposed for estimating the error rates of classifiers. The rationale behind the various estimators and the causes of the sometimes conflicting claims regarding their bias and precision are explore ..."
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Cited by 31 (1 self)
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Several methods (independent subsamples, leaveoneout, crossvalidation, and bootstrapping) have been proposed for estimating the error rates of classifiers. The rationale behind the various estimators and the causes of the sometimes conflicting claims regarding their bias and precision are explored in this paper. The biases and variances of each of the estimators are examined empirically. Crossvalidation, 10fold or greater, seems to be the best approach; the other methods are biased, have poorer precision, or are inconsistent. Though unbiased for linear discriminant classifiers, the 632b bootstrap estimator is biased for nearest neighbors classifiers, more so for single nearest neighbor than for three nearest neighbors. The 632b estimator is also biased for Cartstyle decision trees. Weiss' loo* estimator is unbiased and has better precision than crossvalidation for discriminant and nearest neighbors classifiers, but its lack of bias and improved precision for those classifiers do...
Using Path Diagrams as a Structural Equation Modelling Tool
, 1997
"... this paper, we will show how path diagrams can be used to solve a number of important problems in structural equation modelling. There are a number of problems associated with structural equation modeling. These problems include: ..."
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Cited by 31 (7 self)
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this paper, we will show how path diagrams can be used to solve a number of important problems in structural equation modelling. There are a number of problems associated with structural equation modeling. These problems include:
Bayesian model averaging: development of an improved multiclass, gene selection and classification tool for microarray data
, 2005
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Bayesian Estimation and Testing of Structural Equation Models
 Psychometrika
, 1999
"... The Gibbs sampler can be used to obtain samples of arbitrary size from the posterior distribution over the parameters of a structural equation model (SEM) given covariance data and a prior distribution over the parameters. Point estimates, standard deviations and interval estimates for the parameter ..."
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Cited by 30 (8 self)
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The Gibbs sampler can be used to obtain samples of arbitrary size from the posterior distribution over the parameters of a structural equation model (SEM) given covariance data and a prior distribution over the parameters. Point estimates, standard deviations and interval estimates for the parameters can be computed from these samples. If the prior distribution over the parameters is uninformative, the posterior is proportional to the likelihood, and asymptotically the inferences based on the Gibbs sample are the same as those based on the maximum likelihood solution, e.g., output from LISREL or EQS. In small samples, however, the likelihood surface is not Gaussian and in some cases contains local maxima. Nevertheless, the Gibbs sample comes from the correct posterior distribution over the parameters regardless of the sample size and the shape of the likelihood surface. With an informative prior distribution over the parameters, the posterior can be used to make inferences about the parameters of underidentified models, as we illustrate on a simple errorsinvariables model.
Addressing the Testing Challenge with a WebBased EAssessment System that Tutors as it Assesses
 The 15 th International World Wide Web Conference, Scotland. http://web.cs.wpi.edu/~mfeng/pub/www06.pdf
, 2006
"... Secondary teachers across the country are being asked to use formative assessment data to inform their classroom instruction. At the same time, critics of No Child Left Behind are calling the bill “No Child Left Untested ” emphasizing the negative side of assessment, in that every hour spent assessi ..."
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Cited by 30 (11 self)
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Secondary teachers across the country are being asked to use formative assessment data to inform their classroom instruction. At the same time, critics of No Child Left Behind are calling the bill “No Child Left Untested ” emphasizing the negative side of assessment, in that every hour spent assessing students is an hour lost from instruction. Or does it have to be? What if we better integrated assessment into the classroom, and we allowed students to learn during the test? Maybe we could even provide tutoring on the steps of solving problems. Our hypothesis is that we can achieve more accurate assessment by not only using data on whether students get test items right or wrong, but by also using data on the effort required for students to learn how to solve a test item. We provide evidence for this hypothesis using data collected with our EASSISTment system by more than 600 students over the course of the 20042005 school year. We also show that we can track student knowledge over time using modern longitudinal data analysis techniques. In a separate paper [9], we report on the ASSISTment system’s architecture and scalability, while this paper is focused on how we can reliably assess student learning.