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39
Bayesian Experimental Design: A Review
 Statistical Science
, 1995
"... This paper reviews the literature on Bayesian experimental design, both for linear and nonlinear models. A unified view of the topic is presented by putting experimental design in a decision theoretic framework. This framework justifies many optimality criteria, and opens new possibilities. Various ..."
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Cited by 171 (1 self)
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This paper reviews the literature on Bayesian experimental design, both for linear and nonlinear models. A unified view of the topic is presented by putting experimental design in a decision theoretic framework. This framework justifies many optimality criteria, and opens new possibilities. Various design criteria become part of a single, coherent approach.
Minimum Message Length and Kolmogorov Complexity
 Computer Journal
, 1999
"... this paper is to describe some of the relationships among the different streams and to try to clarify some of the important differences in their assumptions and development. Other studies mentioning the relationships appear in [1, Section IV, pp. 10381039], [2, sections 5.2, 5.5] and [3, p. 465] ..."
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Cited by 104 (25 self)
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this paper is to describe some of the relationships among the different streams and to try to clarify some of the important differences in their assumptions and development. Other studies mentioning the relationships appear in [1, Section IV, pp. 10381039], [2, sections 5.2, 5.5] and [3, p. 465]
Hierarchical Priors and Mixture Models, With Application in Regression and Density Estimation
, 1993
"... ..."
Rational explanation of the selection task
 Psychological Review
, 1996
"... M. Oaksford and N. Chater (O&C; 1994) presented the first quantitative model of P. C. Wason's ( 1966, 1968) selection task in.which performance is rational. J. St B T Evans and D. E. Over (1996) reply that O&C's account is normatively incorrect and cannot model K. N. Kirby's (1994b) or P. Pollard an ..."
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Cited by 46 (4 self)
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M. Oaksford and N. Chater (O&C; 1994) presented the first quantitative model of P. C. Wason's ( 1966, 1968) selection task in.which performance is rational. J. St B T Evans and D. E. Over (1996) reply that O&C's account is normatively incorrect and cannot model K. N. Kirby's (1994b) or P. Pollard and J. St B T Evans's (1983) data. It is argued that an equivalent measure satisfies their normative concerns and that a modification of O&C's model accounts for their empirical concerns. D. Laming (1996) argues that O&C made unjustifiable psychological assumptions and that a "correct" Bayesian analysis agrees with logic. It is argued that O&C's model makes normative and psychological sense and that Laming's analysis is not Bayesian. A. Almor and S. A. Sloman (1996) argue that O&C cannot explain their data. It is argued that Almor and Sloman's data do not bear on O&C's model because they alter the nature of the task. It is concluded that O&C's model remains the most compelling and comprehensive account of the selection task. Research on Wason's (1966, 1968) selection task questions human rationality because performance is not "logically correct?' Recently, Oaksford and Chater (O&C; 1994) provided a rational analysis (Anderson, 1990, 1991) of the selection task that appeared to vindicate human rationality. O&C argued that the selection task is an inductive, rather than a deductive, reasoning task: Participants must assess the truth or falsity of a general rule from specific instances. In particular, participants face a problem of optimal data selection (Lindley, 1956): They must decide which of four cards (p, notp, q, or notq) is likely to provide the most useful data to test a conditional rule,/fp then q. The "logical " solution is to select the p and the notq cards. O&C argued that this solution presupposes falsificationism (Popper, 1959), which argues that only data that can disconfirm, not confirm, hypotheses are of interest. In contrast, O&C's rational analysis uses a Bayesian approach to inductive
NetQuest: A Flexible Framework for LargeScale Network Measurement
, 2006
"... In this paper, we present NetQuest, a flexible framework for largescale network measurement. We apply Bayesian experimental design to select active measurements that maximize the amount of information we gain about the network path properties subject to given resource constraints. We then apply netw ..."
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Cited by 28 (2 self)
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In this paper, we present NetQuest, a flexible framework for largescale network measurement. We apply Bayesian experimental design to select active measurements that maximize the amount of information we gain about the network path properties subject to given resource constraints. We then apply network inference techniques to reconstruct the properties of interest based on the partial, indirect observations we get through these measurements. By casting network measurement in a general Bayesian decision theoretic framework, we achieve flexibility. Our framework can support a variety of design requirements, including (i) differentiated design for providing better resolution to certain parts of the network, (ii) augmented design for conducting additional measurements given existing observations, and (iii) joint design for supporting multiple users who are interested in different parts of the network. Our framework is also scalable and can design measurement experiments that span thousands of routers and end hosts. We develop a toolkit that realizes the framework on PlanetLab. We conduct extensive evaluation using both real traces and synthetic data. Our results show that the approach can accurately estimate networkwide and individual path properties by only monitoring within 210 % of paths. We also demonstrate its effectiveness in providing differentiated monitoring, supporting continuous monitoring, and satisfying the requirements of multiple users.
MCMC methods for continuoustime financial econometrics

, 2003
"... This chapter develops Markov Chain Monte Carlo (MCMC) methods for Bayesian inference in continuoustime asset pricing models. The Bayesian solution to the inference problem is the distribution of parameters and latent variables conditional on observed data, and MCMC methods provide a tool for explor ..."
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Cited by 24 (1 self)
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This chapter develops Markov Chain Monte Carlo (MCMC) methods for Bayesian inference in continuoustime asset pricing models. The Bayesian solution to the inference problem is the distribution of parameters and latent variables conditional on observed data, and MCMC methods provide a tool for exploring these highdimensional, complex distributions. We first provide a description of the foundations and mechanics of MCMC algorithms. This includes a discussion of the CliffordHammersley theorem, the Gibbs sampler, the MetropolisHastings algorithm, and theoretical convergence properties of MCMC algorithms. We next provide a tutorial on building MCMC algorithms for a range of continuoustime asset pricing models. We include detailed examples for equity price models, option pricing models, term structure models, and regimeswitching models. Finally, we discuss the issue of sequential Bayesian inference, both for parameters and state variables.
Exploiting the generic viewpoint assumption
 IJCV
, 1996
"... The ¨generic viewpointässumption states that an observer is not in a special position relative to the scene. It is commonly used to disqualify scene interpretations that assume special viewpoints, following a binary decision that the viewpoint was either generic or accidental. In this paper, we appl ..."
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Cited by 22 (1 self)
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The ¨generic viewpointässumption states that an observer is not in a special position relative to the scene. It is commonly used to disqualify scene interpretations that assume special viewpoints, following a binary decision that the viewpoint was either generic or accidental. In this paper, we apply Bayesian statistics to quantify the probability of a view, and so derive a useful tool to estimate scene parameters. This approach may increase the scope and accuracy of scene estimates. It applies to a range of vision problems. We show shape from shading examples, where we rank shapes or reflectance functions in cases where these are otherwise unknown. The rankings agree with the perceived values.
Bayesian Approach To System Identification
 in Trends and Progress in System Identification, P. Eykhoff, Ed
, 1981
"... Contents 1 Introduction 1 2 Underlying Philosophy and Basic Relations 3 2.1 Two Basic Operations on Uncertainties . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.2 Independent Uncertain Quantities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.3 Derived Relations . . ..."
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Cited by 22 (0 self)
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Contents 1 Introduction 1 2 Underlying Philosophy and Basic Relations 3 2.1 Two Basic Operations on Uncertainties . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.2 Independent Uncertain Quantities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.3 Derived Relations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.4 Additional Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 3 System Model, Reexamined from Bayesian Viewpoint 8 3.1 Discrete White Noise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 3.2 Measurable External Disturbances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 4 Parameter Estimation and Output Prediction 16 4.1 Estimation in Closed Control Loop: Natural Conditions of Control . . . . . . . . . . . . . 18 4.2 OneShot Estimation . . . . . . . . . . . . . . . . . .
Inference and Hierarchical Modeling in the Social Sciences
, 1995
"... this paper I (1) examine three levels of inferential strength supported by typical social science datagathering methods, and call for a greater degree of explicitness, when HMs and other models are applied, in identifying which level is appropriate; (2) reconsider the use of HMs in school effective ..."
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Cited by 21 (6 self)
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this paper I (1) examine three levels of inferential strength supported by typical social science datagathering methods, and call for a greater degree of explicitness, when HMs and other models are applied, in identifying which level is appropriate; (2) reconsider the use of HMs in school effectiveness studies and metaanalysis from the perspective of causal inference; and (3) recommend the increased use of Gibbs sampling and other Markovchain Monte Carlo (MCMC) methods in the application of HMs in the social sciences, so that comparisons between MCMC and betterestablished fitting methodsincluding full or restricted maximum likelihood estimation based on the EM algorithm, Fisher scoring or iterative generalized least squaresmay be more fully informed by empirical practice.
Bayesian Decision Theory, the Maximum Local Mass Estimate, and Color Constancy
 IN PROCEEDINGS: FIFTH INTERNATIONAL CONFERENCE ON COMPUTER VISION, PP 210217, (IEEE COMPUTER
, 1995
"... Vision algorithms are often developed in a Bayesian framework. Two estimators are commonly used: maximum a posteriori (MAP), and minimum mean squared error (MMSE). We argue that neither is appropriate for perception problems. The MAP estimator makes insufficient use of structure in the posterior pro ..."
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Cited by 18 (4 self)
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Vision algorithms are often developed in a Bayesian framework. Two estimators are commonly used: maximum a posteriori (MAP), and minimum mean squared error (MMSE). We argue that neither is appropriate for perception problems. The MAP estimator makes insufficient use of structure in the posterior probability. The squared error penalty of the MMSE estimator does not reflect typical penalties. We describe a new