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77
NonUniform Random Variate Generation
, 1986
"... Abstract. This is a survey of the main methods in nonuniform random variate generation, and highlights recent research on the subject. Classical paradigms such as inversion, rejection, guide tables, and transformations are reviewed. We provide information on the expected time complexity of various ..."
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Cited by 716 (21 self)
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Abstract. This is a survey of the main methods in nonuniform random variate generation, and highlights recent research on the subject. Classical paradigms such as inversion, rejection, guide tables, and transformations are reviewed. We provide information on the expected time complexity of various algorithms, before addressing modern topics such as indirectly specified distributions, random processes, and Markov chain methods.
Probabilistic inference for future climate using an ensemble of climate model evaluations
 Climatic Change
, 2007
"... This paper describes an approach to computing probabilistic assessments of future climate, using a climate model. It clarifies the nature of probability in this context, and illustrates the kinds of judgements that must be made in order for such a prediction to be consistent with the probability cal ..."
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Cited by 33 (12 self)
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This paper describes an approach to computing probabilistic assessments of future climate, using a climate model. It clarifies the nature of probability in this context, and illustrates the kinds of judgements that must be made in order for such a prediction to be consistent with the probability calculus. The climate model is seen as a tool for making probabilistic statements about climate itself, necessarily involving an assessment of the model’s imperfections. A climate event, such as a 2◦C increase in global mean temperature, is identified with a region of ‘climatespace’, and the ensemble of model evaluations is used within a numerical integration designed to estimate the probability assigned to that region.
Some New Perspectives on the Method of Control Variates
 and QuasiMonte Carlo Methods 2000
, 2000
"... The method of control variates is one of the most widely used variance reduction techniques associated with Monte Carlo simulation. This paper studies the method of control variates from several di#erent viewpoints, and establishes new connections between the method of control variates and: conditio ..."
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Cited by 20 (1 self)
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The method of control variates is one of the most widely used variance reduction techniques associated with Monte Carlo simulation. This paper studies the method of control variates from several di#erent viewpoints, and establishes new connections between the method of control variates and: conditional Monte Carlo, antithetics, rotation sampling, stratification, and nonparametric maximum likelihood. We also develop limit theory for the method of control variates under weak assumptions on the estimator of the optimal control coe#cient.
Bayes linear calibrated prediction for complex systems
 Journal of the American Statistical Association
"... A calibrationbased approach is developed for predicting the behaviour of a physical system which is modelled by a computer simulator. The approach is based on Bayes linear adjustment using both system observations and evaluations of the simulator at parameterisations which appear to give good match ..."
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Cited by 18 (13 self)
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A calibrationbased approach is developed for predicting the behaviour of a physical system which is modelled by a computer simulator. The approach is based on Bayes linear adjustment using both system observations and evaluations of the simulator at parameterisations which appear to give good matches to those observations. This approach can be applied to complex highdimensional systems with expensive simulators, where a fullyBayesian approach would be impractical. It is illustrated with an example concerning the collapse of the Thermohaline Circulation (THC) in the Atlantic.
The Joy of Sampling
, 2001
"... . A standard method for handling Bayesian models is to use Markov chain Monte Carlo methods to draw samples from the posterior. We demonstrate this method on two core problems in computer visionstructure from motion and colour constancy. These examples illustrate a samplers producing useful repre ..."
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Cited by 16 (1 self)
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. A standard method for handling Bayesian models is to use Markov chain Monte Carlo methods to draw samples from the posterior. We demonstrate this method on two core problems in computer visionstructure from motion and colour constancy. These examples illustrate a samplers producing useful representations for very large problems. We demonstrate that the sampled representations are trustworthy, using consistency checks in the experimental design. The sampling solution to structure from motion is strictly better than the factorisation approach, because: it reports uncertainty on structure and position measurements in a direct way; it can identify tracking errors; and its estimates of covariance in marginal point position are reliable. Our colour constancy solution is strictly better than competing approaches, because: it reports uncertainty on surface colour and illuminant measurements in a direct way; it incorporates all available constraints on surface reflectance and on illumination in a direct way; and it integrates a spatial model of reflectance and illumination distribution with a rendering model in a natural way. One advantage of a sampled representation is that it can be resampled to take into account other information. We demonstrate the effect of knowing that, in our colour constancy example, a surface viewed in two different images is in fact the same object. We conclude with a general discussion of the strengths and weaknesses of the sampling paradigm as a tool for computer vision. Keywords: Markov chain Monte Carlo, colour constancy, structure from motion 1.
Modeling and compensation of nonlinearities and friction in a micro hard disk drive servo system with nonlinear feedback control
 IEEE Transactions on Control Systems Technology
, 2005
"... Abstract—Friction and nonlinearities result in large residual errors and deteriorate the performance of head positioning of hard disk drive (HDD) servo systems and other mechanical servo systems. Thus, it is highly desirable to characterize the behaviors of nonlinearities and friction in the servo s ..."
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Cited by 9 (3 self)
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Abstract—Friction and nonlinearities result in large residual errors and deteriorate the performance of head positioning of hard disk drive (HDD) servo systems and other mechanical servo systems. Thus, it is highly desirable to characterize the behaviors of nonlinearities and friction in the servo systems. This paper presents a fairly comprehensive modeling and compensation of friction and nonlinearities of a typical voicecoilmotor (VCM) actuator used in commercial HDDs, and the design of an HDD servo system using an enhanced nonlinear control technique. Our contributions are twofold: We will first obtain a complete model of the VCM actuator including friction and nonlinear characteristics through a careful examination of the configuration and structure of the actual system and through a thorough analysis of its physical effects together with its timedomain and frequencydomain responses. We will then proceed to design a servo system for the hard drive using an enhanced composite nonlinear feedback (CNF) control technique with a simple friction and nonlinearity compensation scheme. The enhanced CNF technique has a feature of removing the uncompensated portion of friction and nonlinearities without sacrificing the overall tracking performance. Simulation and experimental results for both the modeling and the servo design show that our approach is very effective and successful. In particular, our experimental results show that the enhanced CNF control has outperformed the conventional proportionalintegralderivative (PID) control in settling time by 76%. We believe that this approach can be adopted to solve other servomechanism problems. Index Terms—Actuators, friction, hard disks, identification, modeling, motion control, nonlinearities, servo systems. I.
Optimality and computations for relative surprise inferences
 Can. J. of Statist
, 2006
"... Relative surprise inferences are based on how beliefs change from a priori to a posteriori. These inferences can be seen to be based on the posterior distribution of the integrated likelihood and, as such, are invariant under relabellings of the parameter of interest. In this paper we demonstrate th ..."
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Cited by 7 (6 self)
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Relative surprise inferences are based on how beliefs change from a priori to a posteriori. These inferences can be seen to be based on the posterior distribution of the integrated likelihood and, as such, are invariant under relabellings of the parameter of interest. In this paper we demonstrate that relative surprise inferences possess an optimality property. Further, computational techniques are developed for implementing these inferences that are applicable whenever we have algorithms to sample from the prior and posterior distributions. 1
Optimal Properties of Some Bayesian Inferences
, 710
"... Abstract: We consider various properties of Bayesian inferences related to repeated sampling interpretations, when we have a proper prior. While these can be seen as particularly relevant when the prior is diffuse, we argue that it is generally reasonable to consider such properties as part of our a ..."
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Cited by 5 (5 self)
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Abstract: We consider various properties of Bayesian inferences related to repeated sampling interpretations, when we have a proper prior. While these can be seen as particularly relevant when the prior is diffuse, we argue that it is generally reasonable to consider such properties as part of our assessment of Bayesian inferences. We discuss the logical implications for how repeated sampling properties should be assessed when we have a proper prior. We develop optimal Bayesian repeated sampling inferences using a generalized idea of what it means for a credible region to contain a false value and discuss the practical use of this idea for error assessment and experimental design. We present results that connect Bayes factors with optimal inferences and develop a generalized concept of unbiasedness for credible regions. Further, we consider the effect of reparameterizations on hpdlike credible regions and argue that one reparameterization is most relevant, when repeated sampling properties and the prior are taken into account. Key words and phrases: repeated sampling properties, relative surprise inferences, priordata conflict, Bayes factors, relative belief ratios. 1
ASYMPTOTIC RESULTS FOR MAXIMUM LIKELIHOOD ESTIMATORS IN JOINT ANALYSIS OF REPEATED MEASUREMENTS AND SURVIVAL TIME
, 2006
"... Maximum likelihood estimation has been extensively used in the joint analysis of repeated measurements and survival time. However, there is a lack of theoretical justification of the asymptotic properties for the maximum likelihood estimators. This paper intends to fill this gap. Specifically, we pr ..."
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Cited by 4 (0 self)
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Maximum likelihood estimation has been extensively used in the joint analysis of repeated measurements and survival time. However, there is a lack of theoretical justification of the asymptotic properties for the maximum likelihood estimators. This paper intends to fill this gap. Specifically, we prove the consistency of the maximum likelihood estimators and derive their asymptotic distributions. The maximum likelihood estimators are shown to be semiparametrically efficient.