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81
Static Detection Of Deadlocks In Polynomial Time
, 1993
"... Parallel and distributed programming languages often include explicit synchronization primitives, such as rendezvous and semaphores. Such programs are subject to synchronization anomalies; the program behaves incorrectly because it has a faulty synchronization structure. A deadlock is an anomaly in ..."
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Cited by 27 (1 self)
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Parallel and distributed programming languages often include explicit synchronization primitives, such as rendezvous and semaphores. Such programs are subject to synchronization anomalies; the program behaves incorrectly because it has a faulty synchronization structure. A deadlock is an anomaly in which some subset of the active tasks of the program mutually wait on each other to advance; thus, the program cannot complete execution. In static anomaly detection, the source code of a program is automatically analyzed to determine if the program can ever exhibit a specific anomaly. Static anomaly detection has the unique advantage that it can certify programs to be free of the tested anomaly; dynamic testing cannot generally do this. Though exact static detection of deadlocks is NPhard [Tay83a], many researchers have tried to detect deadlock by ...
Bayesian model averaging: development of an improved multiclass, gene selection and classification tool for microarray data
, 2005
"... ..."
Variable selection and Bayesian model averaging in casecontrol studies
, 1998
"... Covariate and confounder selection in casecontrol studies is most commonly carried out using either a twostep method or a stepwise variable selection method in logistic regression. Inference is then carried out conditionally on the selected model, but this ignores the model uncertainty implicit in ..."
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Cited by 19 (7 self)
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Covariate and confounder selection in casecontrol studies is most commonly carried out using either a twostep method or a stepwise variable selection method in logistic regression. Inference is then carried out conditionally on the selected model, but this ignores the model uncertainty implicit in the variable selection process, and so underestimates uncertainty about relative risks. We report on a simulation study designed to be similar to actual casecontrol studies. This shows that pvalues computed after variable selection can greatly overstate the strength of conclusions. For example, for our simulated casecontrol studies with 1,000 subjects, of variables declared to be "significant" with pvalues between.01 and.05, only 49 % actually were risk factors when stepwise variable selection was used. We propose Bayesian model averaging as a formal way of taking account of model uncertainty in casecontrol studies. This yields an easily interpreted summary, the posterior probability that a variable is a risk factor, and our simulation study indicates this to be reasonably well calibrated in the situations simulated. The methods are applied and compared
Shotgun stochastic search for “large p” regression
 Journal of the American Statistical Association
, 2007
"... Model search in regression with very large numbers of candidate predictors raises challenges for both model specification and computation, and standard approaches such as Markov chain Monte Carlo (MCMC) and stepwise methods are often infeasible or ineffective. We describe a novel shotgun stochastic ..."
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Cited by 17 (3 self)
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Model search in regression with very large numbers of candidate predictors raises challenges for both model specification and computation, and standard approaches such as Markov chain Monte Carlo (MCMC) and stepwise methods are often infeasible or ineffective. We describe a novel shotgun stochastic search (SSS) approach that explores “interesting” regions of the resulting, very highdimensional model spaces to quickly identify regions of high posterior probability over models. We describe algorithmic and modeling aspects, priors over the model space that induce sparsity and parsimony over and above the traditional dimension penalization implicit in Bayesian and likelihood analyses, and parallel computation using cluster computers. We discuss an example from gene expression cancer genomics, comparisons with MCMC and other methods, and theoretical and simulationbased aspects of performance characteristics in largescale regression model search. We also provide software implementing the methods.
Likelihoodbased Data Squashing: A Modeling Approach to Instance Construction.
, 2002
"... Squashing is a lossy data compression technique that preserves statistical information. Specifically, squashing compresses a massive dataset to a much smaller one so that outputs from statistical analyses carried out on the smaller (squashed) dataset reproduce outputs from the same statistical analy ..."
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Cited by 16 (1 self)
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Squashing is a lossy data compression technique that preserves statistical information. Specifically, squashing compresses a massive dataset to a much smaller one so that outputs from statistical analyses carried out on the smaller (squashed) dataset reproduce outputs from the same statistical analyses carried out on the original dataset. Likelihoodbased data squashing (LDS) differs from a previously published squashing algorithm insofar as it uses a statistical model to squash the data. The results show that LDS provides excellent squashing performance even when the target statistical analysis departs from the model used to squash the data.
Bayesian Model Selection For Time Series Using Markov Chain Monte Carlo
 in ICASSP
"... We present a stochastic simulation technique for subset selection in time series models, based on the use of indicator variables with the Gibbs sampler within a hierarchical Bayesian framework. As an example, the method is applied to the selection of subset linear AR models, in which only significan ..."
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Cited by 10 (3 self)
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We present a stochastic simulation technique for subset selection in time series models, based on the use of indicator variables with the Gibbs sampler within a hierarchical Bayesian framework. As an example, the method is applied to the selection of subset linear AR models, in which only significant lags are included. Joint sampling of the indicators and parameters is found to speed convergence. We discuss the possibility of model mixing where the model is not well determined by the data, and the extension of the approach to include nonlinear model terms. 1. INTRODUCTION Until recently, research into time series modelling has concentrated on those models which are analytically convenient, without necessarily justifying underlying assumptions such as linearity. With rapidly increasing computing power, it is now possible to consider a much wider range of models, including hybrids containing terms from several nonlinear model families. The problem becomes one of subset selection  ...
When do stepwise algorithms meet subset selection criteria?
, 2007
"... Recent results in homotopy and solution paths demonstrate that certain welldesigned greedy algorithms, with a range of values of the algorithmic parameter, can provide solution paths to a sequence of convex optimization problems. On the other hand, in regression many existing criteria in subset sel ..."
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Cited by 9 (3 self)
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Recent results in homotopy and solution paths demonstrate that certain welldesigned greedy algorithms, with a range of values of the algorithmic parameter, can provide solution paths to a sequence of convex optimization problems. On the other hand, in regression many existing criteria in subset selection (including Cp, AIC, BIC, MDL, RIC, etc.) involve optimizing an objective function that contains a counting measure. The two optimization problems are formulated as (P1) and (P0) in the present paper. The latter is generally combinatoric and has been proven to be NPhard. We study the conditions under which the two optimization problems have common solutions. Hence, in these situations a stepwise algorithm can be used to solve the seemingly unsolvable problem. Our main result is motivated by recent work in sparse representation, while two others emerge from different angles: a direct analysis of sufficiency and necessity and a condition on the mostly correlated covariates. An extreme example connected with least angle regression is of independent interest.
Comparison of Machine Learning and Traditional Classifiers in Glaucoma Diagnosis
 IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
, 2002
"... Glaucoma is a progressive optic neuropathy with characteristic structural changes in the optic nerve head reflected in the visual field. The visualfield sensitivity test is commonly used in a clinical setting to evaluate glaucoma. Standard automated perimetry (SAP) is a common computerized visualf ..."
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Cited by 9 (1 self)
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Glaucoma is a progressive optic neuropathy with characteristic structural changes in the optic nerve head reflected in the visual field. The visualfield sensitivity test is commonly used in a clinical setting to evaluate glaucoma. Standard automated perimetry (SAP) is a common computerized visualfield test whose output is amenable to machine learning. We compared the performance of a number of machine learning algorithms with STATPAC indexes mean deviation, pattern standard deviation, and corrected pattern standard deviation. The machine learning algorithms studied included multilayer perceptron (MLP), support vector machine (SVM), and linear (LDA) and quadratic discriminant analysis (QDA), Parzen window, mixture of Gaussian (MOG), and mixture of generalized Gaussian (MGG). MLP and SVM are classifiers that work directly on the decision boundary and fall under the discriminative paradigm. Generative classifiers, which first model the data probability density and then perform classification via Bayes' rule, usually give deeper insight into the structure of the data space. We have applied MOG, MGG, LDA, QDA, and Parzen window to the classification of glaucoma from SAP. Performance of the various classifiers was compared by the areas under their receiver operating characteristic curves and by sensitivities (truepositive rates) at chosen specificities (truenegative rates). The machinelearningtype classifiers showed improved performance over the best indexes from STATPAC. Forwardselection and backwardelimination methodology further improved the classification rate and also has the potential to reduce testing time by diminishing the number of visualfield location measurements.
Bayesian Adaptive Sampling for Variable Selection and Model Averaging
"... For the problem of model choice in linear regression, we introduce a Bayesian adaptive sampling algorithm (BAS), that samples models without replacement from the space of models. For problems that permit enumeration of all models BAS is guaranteed to enumerate the model space in 2 p iterations where ..."
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Cited by 9 (4 self)
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For the problem of model choice in linear regression, we introduce a Bayesian adaptive sampling algorithm (BAS), that samples models without replacement from the space of models. For problems that permit enumeration of all models BAS is guaranteed to enumerate the model space in 2 p iterations where p is the number of potential variables under consideration. For larger problems where sampling is required, we provide conditions under which BAS provides perfect samples without replacement. When the sampling probabilities in the algorithm are the marginal variable inclusion probabilities, BAS may be viewed as sampling models “near ” the median probability model of Barbieri and Berger. As marginal inclusion probabilities are not known in advance we discuss several strategies to estimate adaptively the marginal inclusion probabilities within BAS. We illustrate the performance of the algorithm using simulated and real data and show that BAS can outperform Markov chain Monte Carlo methods. The algorithm is implemented in the R package BAS available at CRAN.