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72
MCMC Methods for Computing Bayes Factors: A Comparative Review
 Journal of the American Statistical Association
, 2000
"... this paper we review several of these methods, and subsequently compare them in the context of two examples, the first a simple regression example, and the second a much more challenging hierarchical longitudinal model of the kind often encountered in biostatistical practice. We find that the joint ..."
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Cited by 30 (1 self)
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this paper we review several of these methods, and subsequently compare them in the context of two examples, the first a simple regression example, and the second a much more challenging hierarchical longitudinal model of the kind often encountered in biostatistical practice. We find that the joint modelparameter space search methods perform adequately but can be difficult to program and tune, while the marginal likelihood methods are often less troublesome and require less in the way of additional coding. Our results suggest that the latter methods may be most appropriate for practitioners working in many standard model choice settings, while the former remain important for comparing large numbers of models, or models whose parameters cannot be easily updated in relatively few blocks. We caution however that all of the methods we compare require significant human and computer effort, suggesting that less formal Bayesian model choice methods may offer a more realistic alternative in many cases.
A WEAKLY INFORMATIVE DEFAULT PRIOR DISTRIBUTION FOR LOGISTIC AND OTHER REGRESSION MODELS
"... We propose a new prior distribution for classical (nonhierarchical) logistic regression models, constructed by first scaling all nonbinary variables to have mean 0 and standard deviation 0.5, and then placing independent Studentt prior distributions on the coefficients. As a default choice, we reco ..."
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Cited by 19 (7 self)
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We propose a new prior distribution for classical (nonhierarchical) logistic regression models, constructed by first scaling all nonbinary variables to have mean 0 and standard deviation 0.5, and then placing independent Studentt prior distributions on the coefficients. As a default choice, we recommend the Cauchy distribution with center 0 and scale 2.5, which in the simplest setting is a longertailed version of the distribution attained by assuming onehalf additional success and onehalf additional failure in a logistic regression. Crossvalidation on a corpus of datasets shows the Cauchy class of prior distributions to outperform existing implementations of Gaussian and Laplace priors. We recommend this prior distribution as a default choice for routine applied use. It has the advantage of always giving answers, even when there is complete separation in logistic regression (a common problem, even when the sample size is large and the number of predictors is small), and also automatically applying more shrinkage to higherorder interactions. This can
Hierarchical Gaussian Process Mixtures for Regression
, 2002
"... this paper, a mixture regression model of Gaussian processes is proposed, and a hybrid Markov chain Monte Carlo (MCMC) algorithm is used for the implementation. If we use this model and algorithm, the computational burden decreases dramatically. A real application is used to illustrate the mixture m ..."
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Cited by 17 (7 self)
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this paper, a mixture regression model of Gaussian processes is proposed, and a hybrid Markov chain Monte Carlo (MCMC) algorithm is used for the implementation. If we use this model and algorithm, the computational burden decreases dramatically. A real application is used to illustrate the mixture model and its implementation
Frailty modeling for spatially correlated survival data, with application to infant mortality in Minnesota
, 2003
"... this paper, we consider random effects corresponding to clusters that are spatially arranged, such as clinical sites or geographical regions. That is, we might suspect that random effects corresponding to strata in closer proximity to each other might also be similar in magnitude. Such spatial arran ..."
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Cited by 16 (5 self)
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this paper, we consider random effects corresponding to clusters that are spatially arranged, such as clinical sites or geographical regions. That is, we might suspect that random effects corresponding to strata in closer proximity to each other might also be similar in magnitude. Such spatial arrangement of the strata can be modeled in several ways, but we group these ways into two general settings: geostatistical approaches, where we use the exact geographic locations (e.g. latitude and longitude) of the strata, and lattice approaches, where we use only the positions of the strata relative to each other (e.g. which counties neighbor which others). We compare our approaches in the context of a dataset on infant mortality in Minnesota counties between 1992 and 1996. Our main substantive goal here is to explain the pattern of infant mortality using important covariates (sex, race, birth weight, age of mother, etc.) while accounting for possible (spatially correlated) differences in hazard among the counties. We use the GIS ArcView to map resulting fitted hazard rates, to help search for possible lingering spatial correlation. The DIC criterion (Spiegelhalter et al.,Journal of the Royal Statistical Society, Series B 2002, to appear) is used to choose among various competing models. We investigate the quality of fit of our chosen model, and compare its results when used to investigate neonatal versus postneonatal mortality. We also compare use of our timetoevent outcome survival model with the simpler dichotomous outcome logistic model. Finally, we summarize our findings and suggest directions for future research
Sequential sampling models of human text classification
 Cognitive Science
, 2003
"... Text classification involves deciding whether or not a document is about a given topic. It is an important problem in machine learning, because automated text classifiers have enormous potential for application in information retrieval systems. It is also an interesting problem for cognitive science ..."
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Cited by 9 (1 self)
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Text classification involves deciding whether or not a document is about a given topic. It is an important problem in machine learning, because automated text classifiers have enormous potential for application in information retrieval systems. It is also an interesting problem for cognitive science, because it involves real world human decision making with complicated stimuli. This paper develops two models of human text document classification based on random walk and accumulator sequential sampling processes. The models are evaluated using data from an experiment where participants classify text documents presented one word at a time under task instructions that emphasize either speed or accuracy, and rate their confidence in their decisions. Fitting the random walk and accumulator models to these data shows that the accumulator provides a better account of the decisions made, and a “balance of evidence ” measure provides the best account of confidence. Both models are also evaluated in the applied information retrieval context, by comparing their performance to established machine learning techniques on the standard Reuters21578 corpus. It is found that they are almost as accurate as the benchmarks, and make decisions much more quickly because they only need to examine a small proportion of the words in the document. In addition, the ability of the accumulator model to produce useful confidence measures is shown to have application in prioritizing the results of classification decisions.
2009), Critical evaluation of parameter consistency and predictive uncertainty in hydrological modelling: a case study using bayesian total error analysis
 Water Resources Research
"... The lack of a robust framework for quantifying the parametric and predictive uncertainty of conceptual rainfall runoff (CRR) models remains a key challenge in hydrology. The Bayesian total error analysis (BATEA) methodology provides a comprehensive framework to hypothesize, infer and evaluate probab ..."
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Cited by 7 (3 self)
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The lack of a robust framework for quantifying the parametric and predictive uncertainty of conceptual rainfall runoff (CRR) models remains a key challenge in hydrology. The Bayesian total error analysis (BATEA) methodology provides a comprehensive framework to hypothesize, infer and evaluate probability models describing input, output and model structural error. This paper assesses the ability of BATEA and standard calibration approaches (standard least squares (SLS) and weighted least squares (WLS)) hal00456158, version 1 12 Feb 2010 to address two key requirements of uncertainty assessment: (i) reliable quantification of predictive uncertainty, and (ii) reliable estimation of parameter uncertainty. The case study presents a challenging calibration of the lumped GR4J model to a catchment with ephemeral responses and large rainfall gradients. Postcalibration diagnostics, including checks of predictive distributions using quantilequantile analysis, suggest that, while still far from perfect, BATEA satisfied its assumed probability models better than SLS and WLS. In addition, WLS/SLS parameter estimates were highly
Decision support systems for police: Lessons from the application of data mining techniques to 'soft' forensic evidence
 APPLICATIONS AND INNOVATIONS IN INTELLIGENT SYSTEMS XII. PROCEEDINGS OF AI2004, THE TWENTYFOURTH SGAI INTERNATIONAL CONFERENCE ON KNOWLEDGE BASED SYSTEMS AND APPLICATIONS OF ARTIFICIAL INTELLIGENCE
, 2004
"... Computer science technology that can support police activities is wide ranging, from the well known geographical information systems display (’pins in maps’), clustering and link analysis algorithms, to the more complex use of data mining technology for profiling single and series of crimes or offen ..."
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Cited by 5 (1 self)
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Computer science technology that can support police activities is wide ranging, from the well known geographical information systems display (’pins in maps’), clustering and link analysis algorithms, to the more complex use of data mining technology for profiling single and series of crimes or offenders, and matching and predicting crimes. This paper presents a discussion of data mining and decision support technologies for police, considering the range of computer science technologies that are available to assist police activities. The discussion is very practical, with examples taken from the authors ’ own work with three United Kingdom police forces. The lessons learned are presented, along with their relevance to future work. We describe significant aspects of the knowledge discovery from databases process, starting with an examination of the data that police collect and the reasons for storing such data, and progressing to the development of crime matching and predictive knowledge which are operationalised in decision support software. Discussion and experimentation include decision support techniques based around spatial statistics, and a wide range of data mining technologies, including casebased reasoning, logic programming and ontologies, survival analysis, Bayesian networks, and the comparison of models that use either behavioural features, spatiotemporal features, or a combination of both. The paper concludes with a discussion of the operational lessons relevant to future work.
Matching and predicting crimes
 In Proceedings of the Twentyfourth SGAI International Conference on Knowledge Based Systems and Applications of Artificial Intelligence (AI2004
, 2004
"... Our central aim is the development of decision support systems based on appropriate technology for such purposes as profiling single and series of crimes or offenders, and matching and predicting crimes. This paper presents research in this area for the highvolume crime of Burglary Dwelling House, ..."
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Cited by 4 (0 self)
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Our central aim is the development of decision support systems based on appropriate technology for such purposes as profiling single and series of crimes or offenders, and matching and predicting crimes. This paper presents research in this area for the highvolume crime of Burglary Dwelling House, with examples taken from the authors ’ own work a United Kingdom police force. Discussion and experimentation include exploratory techniques from spatial statistics and forensic psychology. The crime matching techniques used are casebased reasoning, logic programming and ontologies, and naïve Bayes augmented with spatiotemporal features. The crime prediction techniques are survival analysis and Bayesian networks. 1.
Casedeletion importance sampling estimators: Central limit theorems and related results
, 2005
"... hospitality during her visit. This material is based upon work supported by the National Science Foundation ..."
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Cited by 3 (0 self)
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hospitality during her visit. This material is based upon work supported by the National Science Foundation
Foundations of data mining via granular and rough computing
 Proceedings of the 26th Annual International Computer Software and Applications Conference, COMPSAC’02
, 2002
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