Above are papers related to the following paper: [Paper_ID] Paper #5086. 	[Title] Approximate Bayes Factors and Accounting for Model Uncertainty in Generalized Linear Models [Abstract] Technical Report no. 255 Department of Statistics, University of Washington August 1993; Revised March 1994 Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1131267. 	[Title] Parallel Markov chain Monte Carlo sampling. [Abstract] Markov chain Monte Carlo (MCMC) samplers have proved remarkably popular as tools for Bayesian computation. However, problems can arise in their application when the density of interest is high dimensional and strongly correlated. In these circumstances the sampler may be slow to traverse the state space and mixing is poor. In this article we offer a partial solution to this problem. The state space of the Markov chain is augmented to accommodate multiple chains in parallel. Updates to individual chains are based around a genetic style crossover operator acting on `parent' states drawn from the population of chains. This process makes efficient use of gradient information implicitly encoded within the distribution of states across the population. Empirical studies support the claim that the crossover operator acting on a parallel population of chains improves mixing. This is illustrated with an example of sampling a high dimensional posterior probability density from a complex predictive model. By adopting a latent variable approach the methodology is extended to deal with variable selection and model averaging in high dimensions. This is illustrated with an example of knot selection for a spline interpolant. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #24966. 	[Title] A Practical Bayesian Framework for Backprop Networks [Abstract] A quantitative and practical Bayesian framework is described for learning of mappings in feedforward networks. The framework makes possible: (1) objective comparisons between solutions using alternative network architectures; (2) objective stopping rules for network pruning or growing procedures; (3) objective choice of magnitude and type of weight decay terms or additive regularisers (for penalising large weights, etc.); (4) a measure of the effective number of well-determined parameters in a model; (5) quantified estimates of the error bars on network parameters and on network output; (6) objective comparisons with alternative learning and interpolation models such as splines and radial basis functions. The Bayesian `evidence' automatically embodies `Occam's razor,' penalising over-flexible and over-complex models. The Bayesian approach helps detect poor underlying assumptions in learning models. For learning models well matched to a problem, a good correlation between generalisation ability and the Bayesian evidence is obtained. This paper makes use of the Bayesian framework for regularisation and model comparison described in the companion paper `Bayesian interpolation' (MacKay, 1991a). This framework is due to Gull and Skilling (Gull, 1989a). Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #16008. 	[Title] [12] J. Whittaker. Graphical Models in Applied Mathematical Multivariate Statis- [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #105865. 	[Title] Statistical Ideas for Selecting Network Architectures [Abstract] Choosing the architecture of a neural network is one of the most important problems in making neural networks practically useful, but accounts of applications usually sweep these details under the carpet. How many hidden units are needed? Should weight decay be used, and if so how much? What type of output units should be chosen? And so on. We address these issues within the framework of statistical theory for model This paper is principally concerned with architecture selection issues for feed-forward neural networks (also known as multi-layer perceptrons). Many of the same issues arise in selecting radial basis function networks, recurrent networks and more widely. These problems occur in a much wider context within statistics, and applied statisticians have been selecting and combining models for decades. Two recent discussions are [4, 5]. References [3, 20, 21, 22] discuss neural networks from a statistical perspective. choice, which provides a number of workable approximate answers. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #642827. 	[Title] Decomposable graphical Gaussian model determination [Abstract] We propose a methodology for Bayesian model determination in decomposable graphical Gaussian models. To achieve this aim we consider a hyper inverse Wishart prior distribution on the concentration matrix for each given graph. To ensure compatibility across models, such prior distributions are obtained by marginalisation from the prior conditional on the complete graph. We explore alternative structures for the hyperparameters of the latter, and their consequences for the model. Model determination is carried out by implementing a reversible jump MCMC sampler. In particular, the dimension-changing move we propose involves adding or dropping an edge from the graph. We characterise the set of moves which preserve the decomposability of the graph, giving a fast algorithm for maintaining the junction tree representation of the graph at each sweep. As state variable, we propose to use the incomplete variance-covariance matrix, containing only the elements for which the corresponding element of the inverse is nonzero. This allows all computations to be performed locally, at the clique level, which is a clear advantage for the analysis of large and complex data-sets. Finally, the statistical and computational performance of the procedure is illustrated by means of both artificial and real multidimensional data-sets. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #109323. 	[Title] Estimating Bayes Factors via Posterior Simulation with the Laplace-Metropolis Estimator [Abstract] The key quantity needed for Bayesian hypothesis testing and model selection is the marginal likelihood for a model, also known as the integrated likelihood, or the marginal probability of the data. In this paper we describe a way to use posterior simulation output to estimate marginal likelihoods. We describe the basic Laplace-Metropolis estimator for models without random effects. For models with random effects the compound Laplace-Metropolis estimator is introduced. This estimator is applied to data from the World Fertility Survey and shown to give accurate results. Batching of simulation output is used to assess the uncertainty involved in using the compound Laplace-Metropolis estimator. The method allows us to test for the effects of independent variables in a random effects model, and also to test for the presence of the random effects. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #137873. 	[Title] Stacked Density Estimation [Abstract] Technical Report No. 97-36, Information and Computer Science Department, University of California, Irvine Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1115291. 	[Title] How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis 1 [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1105698. 	[Title] Covariate Selection in Hierarchical Models of Hospital Admission Counts: A Bayes Factor Approach 1 [Abstract] TECHNICAL REPORT No. 268 Department of Statistics, GN-22 University of Washington Seattle, Washington 98195 USA 1 Susan L. Rosenkranz is Pew Health Policy Postdoctoral Fellow at the Institute for Health Policy Studies, Box 0936, University of California at San Francisco, San Francisco, CA 94143, and Adrian E. Raftery is Professor of Statistics and Sociology, Department of Statistics, GN-22, University of Washington, Seattle, WA 98195. Rosenkranz's research was supported by the National Research Service Award 5T32CA 09168-17 from the National Cancer Institute. The authors are grateful to Paula Diehr and Kevin Cain for helpful discussions. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #423463. 	[Title] Model Selection for Generalized Linear Models via GLIB, with Application to Epidemiology 1 [Abstract] 1 This is the first draft of a chapter for Bayesian Biostatistics, edited by Donald A. Berry and Darlene K. Strangl. Adrian E. Raftery is Professor of Statistics and Sociology, Department of Statistics, GN-22, University of Washington, Seattle, WA 98195, USA. Sylvia Richardson is Directeur de Recherche, INSERM Unite 170, 16 avenue Paul Vaillant Couturier, 94807 Villejuif CEDEX, France. Raftery's research was supported by ONR contract no. N-00014-91-J-1074, by the Ministere de la Recherche et de l'Espace, Paris, by the Universite de Paris VI, and by INRIA, Rocquencourt, France. Raftery thanks the latter two institutions, Paul Deheuvels and Gilles Celeux for hearty hospitality during his Paris sabbatical in which part of this chapter was written. The authors are grateful to Christine Montfort for excellent research assistance and to Mariette Gerber, Michel Chavance and David Madigan for helpful discussions. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #280876. 	[Title] Principal Curve Clustering With Noise [Abstract] Technical Report 317 Department of Statistics University of Washington. 1 Derek Stanford is Graduate Research Assistant and Adrian E. Raftery is Professor of Statistics and Sociology, both at the Department of Statistics, University of Washington, Box 354322, Seattle, WA 98195-4322, USA. E-mail: stanford@stat.washington.edu and raftery@stat.washington.edu. Web: http://www.stat.washington.edu/raftery. This research was supported by ONR grants N00014-96-1-0192 and N00014-96-1-0330. The authors are grateful to Simon Byers, Gilles Celeux and Christian Posse for helpful discussions. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #3191. 	[Title] Model Selection and Accounting for Model Uncertainty in Linear Regression Models [Abstract] 1 Adrian E. Raftery is Professor of Statistics and Sociology, David Madigan is Assistant Professor of Statistics, and Jennifer Hoeting is a Ph.D. Candidate, all at the Department of Statistics, GN-22, University of Washington, Seattle, WA 98195. The research of Raftery and Hoeting was supported by ONR Contract N-00014-91-J-1074. Madigan's research was partially supported by NSF grant no. DMS 92111627. The authors are grateful to Danika Lew for research assistance. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #308920. 	[Title] A Graphical Characterization of Lattice Conditional Independence Models [Abstract] Lattice conditional independence (LCI) models for multivariate normal data recently have been introduced for the analysis of non-monotone missing data patterns and of nonnested dependent linear regression models ( seemingly unrelated regressions). It is shown here that the class of LCI models coincides with a subclass of the class of graphical Markov models determined by acyclic digraphs (ADGs), namely, the subclass of transitive ADG models. An explicit graph - theoretic characterization of those ADGs that are Markov equivalent to some transitive ADG is obtained. This characterization allows one to determine whether a specific ADG D is Markov equivalent to some transitive ADG, hence to some LCI model, in polynomial time, without an exhaustive search of the (exponentially large) equivalence class [D ]. These results do not require the existence or positivity of joint densities. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #91581. 	[Title] Finding Overlapping Distributions with MML [Abstract] This paper considers an aspect of mixture modelling. Significantly overlapping distributions require more data for their parameters to be accurately estimated than well separated distributions. For example, two Gaussian distributions are considered to significantly overlap when their means are within three standard deviations of each other. If insufficient data is available, only a single component distribution will be estimated, although the data originates from two component distributions. We consider how much data is required to distinguish two component distributions from one distribution in mixture modelling using the minimum message length (MML) criterion. First, we perform experiments which show the MML criterion performs well relative to other Bayesian criteria. Second, we make two improvements to the existing MML estimates, that improve its performance with overlapping distributions. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #79809. 	[Title] MDL and MML Similarities and Differences (Introduction to Minimum Encoding Inference Part III) [Abstract] Tech Report 207 Department of Computer Science, Monash University, Clayton, Vic. 3168, Australia Abstract: This paper continues the introduction to minimum encoding inductive inference given by Oliver and Hand. This series of papers was written with the objective of providing an introduction to this area for statisticians. We describe the message length estimates used in Wallace's Minimum Message Length (MML) inference and Rissanen's Minimum Description Length (MDL) inference. The differences in the message length estimates of the two approaches are explained. The implications of these differences for applications are discussed. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #645571. 	[Title] A Note on the Dirichlet Process Prior in Bayesian Nonparametric Inference with Partial Exchangeability 1 [Abstract] Technical Report no. 297 Department of Statistics University of Washington 1 Sonia Petrone is Assistant Professor, Universita di Pavia, Dipartimento di Economia Politica e Metodi Quantitativi, I-27100 Pavia, Italy and Adrian E. Raftery is Professor of Statistics and Sociology, Department of Statistics, University of Washington, Box 354322, Seattle, WA 98195-4322. This research was supported by ONR grant no. N-00014-91-J-1074 and by grants from MURST, Rome. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #79817. 	[Title] Bayesian and Information-Theoretic Priors for Bayesian Network Parameters [Abstract] We consider Bayesian and information-theoretic approaches for determining non-informative prior distributions in a parametric model family. The information-theoretic approaches are based on the recently modified definition of stochastic complexity by Rissanen, and on the Minimum Message Length (MML) approach by Wallace. The Bayesian alternatives include the uniform prior, and the equivalent sample size priors. In order to be able to empirically compare the different approaches in practice, the methods are instantiated for a model family of practical importance, the family of Bayesian networks. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1131466. 	[Title] Stochastic Complexity Based Estimation of Missing Elements in Questionnaire Data [Abstract] In this paper we study a new information-theoretically justified approach to missing data estimation for multivariate categorical data. The approach discussed is a model-based imputation procedure relative to a model class (i.e., a functional form for the probability distribution of the complete data matrix), which in our case is the set of multinomial models with some independence assumptions. Based on the given model class assumption an information-theoretic criterion can be derived to select between the different complete data matrices. Intuitively this general criterion, called stochastic complexity, represents the shortest code length needed for coding the complete data matrix relative to the model class chosen. Using this information-theoretic criteria, the missing data problem is reduced to a search problem, i.e., finding the data completion with minimal stochastic complexity. In the experimental part of the paper we present empirical results of the approach using two real data sets, and compare these results to those achived by commonly used techniques such as case deletion and imputating sample averages. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #11342. 	[Title] On Pruning and Averaging Decision Trees [Abstract] Pruning a decision tree is considered by some researchers to be the most important part of tree building in noisy domains. While, there are many approaches to pruning, an alternative approach of averaging over decision trees has not received as much attention. We perform an empirical comparison of pruning with the approach of averaging over decision trees. For this comparison we use a computa-tionally efficient method of averaging, namely averaging over the extended fanned set of a tree. Since there are a wide range of approaches to pruning, we compare tree averaging with a traditional pruning approach, along with an optimal pruning approach. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1118546. 	[Title] Hierarchical Selection Models with Applications in Meta-Analysis [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #358866. 	[Title] BAYESIAN ESTIMATION OF THE VON MISES CONCENTRATION PARAMETER [Abstract] The von Mises distribution is a maximum entropy distribution. It corresponds to the distribution of an angle of a compass needle in a uniform magnetic field of direction, , with concentration parameter, . The concentration parameter, , is the ratio of the field strength to the temperature of thermal fluctuations. Previously, we obtained a Bayesian estimator for the von Mises distribution parameters using the information-theoretic Minimum Message Length (MML) principle. Here, we examine a variety of Bayesian estimation techniques by examining the posterior distribution in both polar and Cartesian co-ordinates. We compare the MML estimator with these fellow Bayesian techniques, and a range of Classical estimators. We find that the Bayesian estimators outperform the Classical estimators. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #354004. 	[Title] Formal Rules for Selecting Prior Distributions: A Review and Annotated Bibliography [Abstract] Subjectivism has become the dominant philosophical foundation for Bayesian inference. Yet, in practice, most Bayesian analyses are performed with so-called "noninfor-mative" priors, that is, priors constructed by some formal rule. We review the plethora of techniques for constructing such priors, and discuss some of the practical and philosophical issues that arise when they are used. We give special emphasis to Jeffreys's rules and discuss the evolution of his point of view about the interpretation of priors, away from unique representation of ignorance toward the notion that they should be chosen by convention. We conclude that the problems raised by the research on priors chosen by formal rules are serious and may not be dismissed lightly; when sample sizes are small (relative to the number of parameters being estimated) it is dangerous to put faith in any "default" solution; but when asymptotics take over, Jeffreys's rules and their variants remain reasonable choices. We also provide an annotated bibliography. fl Robert E. Kass is Professor and Larry Wasserman is Associate Professor, Department of Statistics, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213-2717. The work of both authors was supported by NSF grant DMS-9005858 and NIH grant R01-CA54852-01. The authors thank Nick Polson for helping with a few annotations, and Jim Berger, Teddy Seidenfeld and Arnold Zellner for useful comments and discussion. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #561364. 	[Title] Markov Chain Monte Carlo Model Determination for Hierarchical and Graphical Log-linear Models [Abstract] The Bayesian approach to comparing models involves calculating the posterior probability of each plausible model. For high-dimensional contingency tables, the set of plausible models is very large. We focus attention on reversible jump Markov chain Monte Carlo (Green, 1995) and develop strategies for calculating posterior probabilities of hierarchical, graphical or decomposable log-linear models. Even for tables of moderate size, these sets of models may be very large. The choice of suitable prior distributions for model parameters is also discussed in detail, and two examples are presented. For the first example, a 2 fi 3 fi 4 table, the model probabilities calculated using our reversible jump approach are compared with model probabilities calculated exactly or by using an alternative approximation. The second example is a 2 6 contingency table for which exact methods are infeasible, due to the large number of possible models. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #310742. 	[Title] Bayesian Graphical Modeling for Intelligent Tutoring Systems [Abstract] Conventional Intelligent Tutoring Systems (ITS) do not acknowledge uncertainty about the student's knowledge. Yet, both the outcome of any teaching intervention and the exact state of the student's knowledge are uncertain. In recent years, researchers have made startling progress in the management of uncertainty in knowledge-based systems. Building on these developments, we describe an ITS architecture that explicitly models uncertainty. This will facilitate more accurate student modeling and provide ITS's which can learn. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #39890. 	[Title] A THEORY OF INFERRED CAUSATION perceive causal relationships in uncon trolled observations. 2. the task [Abstract] This paper concerns the empirical basis of causation, and addresses the following issues: We propose a minimal-model semantics of causation, and show that, contrary to common folklore, genuine causal influences can be distinguished from spurious covariations following standard norms of inductive reasoning. We also establish a sound characterization of the conditions under which such a distinction is possible. We provide an effective algorithm for inferred causation and show that, for a large class of data the algorithm can uncover the direction of causal influences as defined above. Finally, we ad dress the issue of non-temporal causation. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1121659. 	[Title] Causal Discovery via MML [Abstract] Automating the learning of causal models from sample data is a key step toward incorporating machine learning in the automation of decision-making and reasoning under uncertainty. This paper presents a Bayesian approach to the discovery of causal models, using a Minimum Message Length (MML) method. We have developed encoding and search methods for discovering linear causal models. The initial experimental results presented in this paper show that the MML induction approach can recover causal models from generated data which are quite accurate reflections of the original models; our results compare favorably with those of the TETRAD II program of Spirtes et al. [25] even when their algorithm is supplied with prior temporal information and MML is not. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #17242. 	[Title] Detecting Features in Spatial Point Processes with Clutter via Model-Based Clustering [Abstract] Technical Report No. 295 Department of Statistics, University of Washington October, 1995 1 Abhijit Dasgupta is a graduate student at the Department of Biostatistics, University of Washington, Box 357232, Seattle, WA 98195-7232, and his e-mail address is dasgupta@biostat.washington.edu. Adrian E. Raftery is Professor of Statistics and Sociology, Department of Statistics, University of Washington, Box 354322, Seattle, WA 98195-4322, and his e-mail address is raftery@stat.washington.edu. This research was supported by Office of Naval Research Grant no. N-00014-91-J-1074. The authors are grateful to Peter Guttorp, Girardeau Henderson and Robert Muise for helpful discussions. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1106789. 	[Title] Model Selection for Consumer Loan Application Data [Abstract] Loan applications at banks are often long, requiring the applicant to provide large amounts of data. Is all of it necessary? Can we save the applicant some frustration and the bank some expense by using only a subset of the relevant variables? To answer this question, I have attempted to model the current loan approval process at a particular bank. I have used several model selection techniques for logistic regression, including stepwise regression, Occam's Window, Markov Chain Monte Carlo Model Composition (Raftery, Madigan, and Hoeting, 1993), and Bayesian Random Searching. The resulting models largely agree upon a subset of only one-third of the original variables. fl This paper was completed in partial fulfillment of the Ph.D. data analysis requirement. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #153063. 	[Title] Bayesian Forecasting of Multinomial Time Series through Conditionally Gaussian Dynamic Models [Abstract] Claudia Cargnoni is with the Dipartimento Statistico, Universita di Firenze, 50100 Firenze, Italy. Peter Muller is Assistant Professor, and Mike West is Professor, in the Institute of Statistics and Decision Sciences at Duke University, Durham NC 27708-0251. Research of Cargnoni was performed while visiting ISDS during 1995. Muller and West were partially supported by NSF under grant DMS-9305699. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #162664. 	[Title] An Algorithm for the Construction of Bayesian Network Structures from Data [Abstract] Previous algorithms for the construction of Bayesian belief network structures from data have been either highly dependent on conditional independence (CI) tests, or have required an ordering on the nodes to be supplied by the user. We present an algorithm that integrates these two approaches - CI tests are used to generate an ordering on the nodes from the database which is then used to recover the underlying Bayesian network structure using a non CI based method. Results of preliminary evaluation of the algorithm on two networks (ALARM and LED) are presented. We also discuss some algo rithm performance issues and open problems. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #129896. 	[Title] Accounting for Model Uncertainty in Survival Analysis Improves Predictive Performance [Abstract] Survival analysis is concerned with finding models to predict the survival of patients or to assess the efficacy of a clinical treatment. A key part of the model-building process is the selection of the predictor variables. It is standard to use a stepwise procedure guided by a series of significance tests to select a single model, and then to make inference conditionally on the selected model. However, this ignores model uncertainty, which can be substantial. We review the standard Bayesian model averaging solution to this problem and extend it to survival analysis, introducing partial Bayes factors to do so for the Cox proportional hazards model. In two examples, taking account of model uncertainty enhances predictive performance, to an extent that could be clinically useful. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #358887. 	[Title] SINGLE FACTOR ANALYSIS BY MML ESTIMATION [Abstract] The Minimum Message Length (MML) technique is applied to the problem of estimating the parameters of a multivariate Gaussian model in which the correlation structure is modelled by a single common factor. Implicit estimator equations are derived and compared with those obtained from a Maximum Likelihood (ML) analysis. Unlike ML, the MML estimators remain consistent when used to estimate both the factor loadings and factor scores. Tests on simulated data show the MML estimates to be on av erage more accurate than the ML estimates when the former exist. If the data show little evidence for a factor, the MML estimate collapses. It is shown that the condition for the existence of an MML estimate is essentially that the log likelihood ratio in favour of the factor model exceed the value expected under the null (no-factor) hypotheses. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #129897. 	[Title] The out-of-bootstrap method for model averaging and selection [Abstract] We propose a bootstrap-based method for model averaging and selection that focuses on training points that are left out of individual bootstrap samples. This information can be used to estimate optimal weighting factors for combining estimates from different bootstrap samples, and also for finding the best subsets the linear model setting. These proposals provide alternatives to Bayesian approaches to model averaging and selection, requiring less computation and fewer subjective choices. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1127530. 	[Title] Bayesian inference for nondecomposable graphical Gaussian models [Abstract] In this paper we propose a method to calculate the posterior probability of a nondecomposable graphical Gaussian model. Our proposal is based on a new device to sample from Wishart distributions, conditional on the graphical constraints. As a result, our methodology allows Bayesian model selection within the whole class of graphical Gaussian models, including nondecomposable ones. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #440815. 	[Title] Change Point and Change Curve Modeling in Stochastic Processes and Spatial Statistics [Abstract] 1 This article will appear in Volume 1, no. 4 (1994) of Journal of Applied Statistical Science. Adrian E. Raftery is Professor of Statistics and Sociology, Department of Statistics, GN-22, University of Washington, Seattle, WA 98195. This research was supported by ONR contract no. N-00014-91-J-1074, by NIH Grant no. 5R01HD26330-02, by the Ministere de la Recherche et de l'Espace, Paris, by the Universite de Paris VI, and by INRIA, Rocquencourt, France. Raftery thanks the latter two institutions, Paul Deheuvels and Gilles Celeux for hearty hospitality during his Paris sabbatical in which this article was written. This article was prepared for presentation at the Conference on Applied Change Point Analysis, University of Maryland-Baltimore, March 17-18, 1993. Parts of this article review collaborative research with others to whom I would like to express my appreciation, namely Volkan Akman, Jeff Banfield, Nhu Le, Steven Lewis, Doug Martin, Fionn Murtagh, Ross Taplin and Simon Tavare. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1110000. 	[Title] FLEXIBLE PARAMETRIC MEASUREMENT ERROR MODELS [Abstract] Inferences in measurement error models can be sensitive to modeling assumptions. Specifically, if the model is incorrect then the estimates can be inconsistent. To reduce sensitivity to modeling assumptions and yet still retain the efficiency of parametric inference we propose to use flexible parametric models which can accommodate departures from standard parametric models. We use mixtures of normals for this purpose. We study two cases in detail: a linear errors-in-variables model and a change-point Berkson model. fl Raymond J. Carroll is Professor of Statistics, Nutrition and Toxicology, Department of Statistics, Texas A&M University, College Station, TX 77843-3143. Kathryn Roeder is Associate Professor, and Larry Wasser-man is Professor, Department of Statistics, Carnegie-Mellon University, Pittsburgh PA 15213-3890. Carroll's research was supported by a grant from the National Cancer Institute (CA-57030). Roeder's research was supported by NSF grant DMS-9496219. Wasserman's research was supported by NIH grant RO1-CA54852 and NSF grants DMS-9303557 and DMS-9357646. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #3187. 	[Title] A Reference Bayesian Test for Nested Hypotheses And its Relationship to the Schwarz Criterion [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #15987. 	[Title] "Linear Dependencies Represented by Chain Graphs," "Graphical Modelling With MIM," Manual. "Identifying Independence in Bayesian [Abstract] 8] Dori, D. and Tarsi, M., "A Simple Algorithm to Construct a Consistent Extension of a Partially Oriented Graph," Computer Science Department, Tel-Aviv University. Also Technical Report R-185, UCLA, Cognitive Systems Laboratory, October 1992. [14] Pearl, J. and Wermuth, N., "When Can Association Graphs Admit a Causal Interpretation?," UCLA, Cognitive Systems Laboratory, Technical Report R-183-L, November 1992. [17] Verma, T.S. and Pearl, J., "Deciding Morality of Graphs is NP-complete," Technical Report R-188, UCLA, Cognitive Systems Laboratory, October 1992. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #31863. 	[Title] Unsupervised Learning Using MML [Abstract] This paper discusses the unsupervised learning problem. An important part of the unsupervised learning problem is determining the number of constituent groups (components or classes) which best describes some data. We apply the Minimum Message Length (MML) criterion to the unsupervised learning problem, modifying an earlier such MML application. We give an empirical comparison of criteria prominent in the literature for estimating the number of components in a data set. We conclude that the Minimum Message Length criterion performs better than the alternatives on the data considered here for unsupervised learning tasks. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #3192. 	[Title] Bayesian Graphical Models for Discrete Data [Abstract] z York's research was supported by a NSF graduate fellowship. The authors are grateful to Julian Besag, David Bradshaw, Jeff Bradshaw, James Carlsen, David Draper, Ivar Heuch, Robert Kass, Augustine Kong, Steffen Lauritzen, Adrian Raftery, and James Zidek for helpful comments and discussions. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #137849. 	[Title] Inference in Model-Based Cluster Analysis [Abstract] Technical Report no. 285 Department of Statistics University of Washington. March 10, 1995 Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1153275. 	[Title]  [Abstract] Tech Report 4-94 Department of Statistics, Open University, Walton Hall, MK7 6AA, UK Tech Report 205 Department of Computer Science, Monash University, Clayton, Vic. 3168, Australia Abstract: This paper examines the minimum encoding approaches to inference, Minimum Message Length (MML) and Minimum Description Length (MDL). This paper was written with the objective of providing an introduction to this area for statisticians. We describe coding techniques for data, and examine how these techniques can be applied to perform inference and model selection. Please summarise all the given information.

Paper #5086 has the title `Approximate Bayes Factors and Accounting for Model Uncertainty in Generalized Linear Models  
` and the abstract `Technical Report no. 255 Department of Statistics, University of Washington August 1993; Revised March 1994 
`.
Question: Which category should Paper #5086 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #5086 has the title `Approximate Bayes Factors and Accounting for Model Uncertainty in Generalized Linear Models  
` and the abstract `Technical Report no. 255 Department of Statistics, University of Washington August 1993; Revised March 1994 
`.
Question: Which category should Paper #5086 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Probabilistic_Methods