Above are papers related to the following paper: [Paper_ID] Paper #561595. 	[Title] Exploratory Modelling of Multiple Non-Stationary Time Series: Latent Process Structure and Decompositions [Abstract] We describe and illustrate Bayesian approaches to modelling and analysis of multiple non-stationary time series. This begins with uni-variate models for collections of related time series assumedly driven by underlying but unobservable processes, referred to as dynamic latent factor processes. We focus on models in which the factor processes, and hence the observed time series, are modelled by time-varying autoregressions capable of flexibly representing ranges of observed non-stationary characteristics. We highlight concepts and new methods of time series decomposition to infer characteristics of latent components in time series, and relate uni-variate decomposition analyses to underlying multivariate dynamic factor structure. Our motivating application is in analysis of multiple EEG traces from an ongoing EEG study at Duke. In this study, individuals undergoing ECT therapy generate multiple EEG traces at various scalp locations, and physiological interest lies in identifying dependencies and dissimilarities across series. In addition to the multivariate and non-stationary aspects of the series, this area provides illustration of the new results about decomposition of time series into latent, physically interpretable components; this is illustrated in data analysis of one EEG data set. The paper also discusses current and future research directions. fl This research was supported in part by the National Science Foundation under grant DMS-9311071. The EEG data and context arose from discussions with Dr Andrew Krystal, of Duke University Medical Center, with whom continued interactions have been most valuable. Address for correspondence: Institute of Statistics and Decision Sciences, Duke University, Durham, NC 27708-0251 U.S.A. (http://www.stat.duke.edu) Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #561568. 	[Title] Analysis of hospital quality monitors using hierarchical time series models [Abstract] The VA management services department invests considerably in the collection and assessment of data to inform on hospital and care-area specific levels of quality of care. Resulting time series of quality monitors provide information relevant to evaluating patterns of variability in hospital-specific quality of care over time and across care areas, and to compare and assess differences across hospitals. In collaboration with the VA management services group we have developed various models for evaluating such patterns of dependencies and combining data across the VA hospital system. This paper provides a brief overview of resulting models, some summary examples on three monitor time series, and discussion of data, modelling and inference issues. This work introduces new models for multivariate non-Gaussian time series. The framework combines cross-sectional, hierarchical models of the population of hospitals with time series structure to allow and measure time-variations in the associated hierarchical model parameters. In the VA study, the within-year components of the models describe patterns of heterogeneity across the population of hospitals and relationships among several such monitors, while the time series components describe patterns of variability through time in hospital-specific effects and their relationships across quality monitors. Additional model components isolate unpredictable aspects of variability in quality monitor outcomes, by hospital and care areas. We discuss model assessment, residual analysis and MCMC algorithms developed to fit these models, which will be of interest in related applications in other socio-economic areas. 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 #646440. 	[Title] Signal Processing and Communications Reversible Jump Sampler for Autoregressive Time Series, Employing Full Conditionals to [Abstract] Technical Report CUED/F-INFENG/TR. 304 We use reversible jump Markov chain Monte Carlo (MCMC) methods (Green 1995) to address the problem of model order uncertainty in au-toregressive (AR) time series within a Bayesian framework. Efficient model jumping is achieved by proposing model space moves from the full conditional density for the AR parameters, which is obtained analytically. This is compared with an alternative method, for which the moves are cheaper to compute, in which proposals are made only for the new parameters in each move. Results are presented for both synthetic and audio time series. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #38537. 	[Title] BAYESIAN STATISTICS 6, pp. 000--000  Exact sampling for Bayesian inference: towards general purpose algorithms [Abstract] There are now methods for organising a Markov chain Monte Carlo simulation so that it can be guaranteed that the state of the process at a given time is exactly drawn from the target distribution. The question of assessing convergence totally vanishes. Such methods are known as exact or perfect sampling. The approach that has received most attention uses the protocol of coupling from the past devised by Propp and Wilson (Random Structures and Algorithms,1996), in which multiple dependent paths of the chain are run from different initial states at a sequence of initial times going backwards into the past, until they satisfy the condition of coalescence by time 0. When this is achieved the state at time 0 is distributed according to the required target. This process must be implemented very carefully to assure its validity (including appropriate re-use of random number streams), and also requires one of various tricks to enable us to follow infinitely many sample paths with a finite amount of work. With the ultimate objective of Bayesian MCMC with guaranteed convergence, the purpose of this paper is to describe recent efforts to construct exact sampling methods for continuous-state Markov chains. We review existing methods based on gamma-coupling and rejection sampling (Murdoch and Green, Scandinavian Journal of Statistics, 1998), that are quite straightforward to understand, but require a closed form for the transition kernel and entail cumbersome algebraic manipulation. We then introduce two new methods based on random walk Metropolis, that offer the prospect of more automatic use, not least because the difficult, continuous, part of the transition mechanism can be coupled in a generic way, using a proposal distribution of convenience. One of the methods is based on a neat decomposition of any unimodal (multivariate) symmetric density into pieces that may be re-assembled to construct any translated copy of itself: that allows coupling of a continuum of Metropolis proposals to a finite set, at least for a compact state space. We discuss methods for economically coupling the subsequent accept/reject decisions. Our second new method deals with unbounded state spaces, using a trick due to W. S. Kendall of running a coupled dominating process in parallel with the sample paths of interest. The random subset of the state space below the dominating path is compact, allowing efficient coupling and coalescence. We look towards the possibility that application of such methods could become sufficiently convenient that they could become the basis for routine Bayesian computation in the foreseeable future. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #561674. 	[Title] Studies of Neurological Transmission Analysis using Hierarchical Bayesian Mixture Models [Abstract] Hierarchically structured mixture models are studied in the context of data analysis and inference on neural synaptic transmission characteristics in mammalian, and other, central nervous systems. Mixture structures arise due to uncertainties about the stochastic mechanisms governing the responses to electro-chemical stimulation of individual neuro-transmitter release sites at nerve junctions. Models attempt to capture scientific features such as the sensitivity of individual synaptic transmission sites to electro-chemical stimuli, and the extent of their electro-chemical responses when stimulated. This is done via suitably structured classes of prior distributions for parameters describing these features. Such priors may be structured to permit assessment of currently topical scientific hypotheses about fundamental neural function. Posterior analysis is implemented via stochastic simulation. Several data analyses are described to illustrate the approach, with resulting neurophysiological insights in some recently generated experimental contexts. Further developments and open questions, both neurophysiological and statistical, are noted. Research partially supported by the NSF under grants DMS-9024793, DMS-9305699 and DMS-9304250. This work represents part of a collaborative project with Dr Dennis A Turner, of Duke University Medical Center and Durham VA. Data was provided by Dr Turner and by Dr Howard V Wheal of Southampton University. A slightly revised version of this paper is published in the Journal of the American Statistical Association (vol 92, pp587-606), under the modified title Hierarchical Mixture Models in Neurological Transmission Analysis. The author is the recipient of the 1997 Mitchell Prize for "the Bayesian analysis of a substantive and concrete problem" based on the work reported in this paper. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #561610. 	[Title] BAYESIAN TIME SERIES: Models and Computations for the Analysis of Time Series in the Physical Sciences [Abstract] This articles discusses developments in Bayesian time series mod-elling and analysis relevant in studies of time series in the physical and engineering sciences. With illustrations and references, we discuss: Bayesian inference and computation in various state-space models, with examples in analysing quasi-periodic series; isolation and modelling of various components of error in time series; decompositions of time series into significant latent subseries; nonlinear time series models based on mixtures of auto-regressions; problems with errors and uncertainties in the timing of observations; and the development of non-linear models based on stochastic deformations of time scales. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #561611. 	[Title] Modelling and robustness issues in Bayesian time series analysis [Abstract] Some areas of recent development and current interest in time series are noted, with some discussion of Bayesian modelling efforts motivated by substantial practical problems. The areas include non-linear auto-regressive time series modelling, measurement error structures in state-space modelling of time series, and issues of timing uncertainties and time deformations. Some discussion of the needs and opportunities for work on non/semi-parametric models and robustness issues is given in each context. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #561581. 	[Title] Priors and Component Structures in Autoregressive Time Series Models [Abstract] New approaches to prior specification and structuring in autoregressive time series models are introduced and developed. We focus on defining classes of prior distributions for parameters and latent variables related to latent components of an autoregressive model for an observed time series. These new priors naturally permit the incorporation of both qualitative and quantitative prior information about the number and relative importance of physically meaningful components that represent low frequency trends, quasi-periodic sub-processes, and high frequency residual noise components of observed series. The class of priors also naturally incorporates uncertainty about model order, and hence leads in posterior analysis to model order assessment and resulting posterior and predictive inferences that incorporate full uncertainties about model order as well as model parameters. Analysis also formally incorporates uncertainty, and leads to inferences about, unknown initial values of the time series, as it does for predictions of future values. Posterior analysis involves easily implemented iterative simulation methods, developed and described here. One motivating applied field is climatology, where the evaluation of latent structure, especially quasi-periodic structure, is of critical importance in connection with issues of global climatic variability. We explore analysis of data from the Southern Oscillation Index (SOI), one of several series that has been central in recent high-profile debates in the atmospheric sciences about recent apparent trends in climatic indicators. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #561582. 	[Title] Bayesian Inference on Periodicities and Component Spectral Structure in Time Series [Abstract] Summary We detail and illustrate time series analysis and spectral inference in autoregressive models with a focus on the underlying latent structure and time series decompositions. A novel class of priors on parameters of latent components leads to a new class of smoothness priors on autoregressive coefficients, provides for formal inference on model order, including very high order models, and leads to the incorporation of uncertainty about model order into summary inferences. The class of prior models also allows for subsets of unit roots, and hence leads to inference on sustained though stochastically time-varying periodicities in time series. Applications to analysis of the frequency composition of time series, in both time and spectral domains, is illustrated in a study of a time series from astronomy. This analyses demonstrates the impact and utility of the new class of priors in addressing model order uncertainty and in allowing for unit root structure. Time domain decomposition of a time series into estimated latent components provides an important alternative view of the component spectral characteristics of a series. In addition, our data analysis illustrates the utility of the smoothness prior and allowance for unit root structure in inference about spectral densities. In particular, the framework overcomes supposed problems in spectral estimation with autoregressive models using more traditional model fitting methods. 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 #561613. 	[Title] STUDIES OF QUALITY MONITOR TIME SERIES: THE V.A. HOSPITAL SYSTEM build on foundational contributions in [Abstract] This report describes statistical research and development work on hospital quality monitor data sets from the nationwide VA hospital system. The project covers statistical analysis, exploration and modelling of data from several quality monitors, with the primary goals of: (a) understanding patterns of variability over time in hospital-level and monitor area specific quality monitor measures, and (b) understanding patterns of dependencies between sets of monitors. We present discussion of basic perspectives on data structure and preliminary data exploration for three monitors, followed by developments of several classes of formal models. We identify classes of hierarchical random effects time series models to be of relevance in mod-elling single or multiple monitor time series. We summarise basic model features and results of analyses of the three monitor data sets, in both single and multiple monitor frameworks, and present a variety of summary inferences in graphical displays. Our discussion includes summary conclusions related to the two key goals, discussions of questions of comparisons across hospitals, and some recommendations about further potential substantive and statistical investigations. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1131223. 	[Title] On Sequential Simulation-Based Methods for Bayesian Filtering [Abstract] In this report, we present an overview of sequential simulation-based methods for Bayesian filtering of nonlinear and non-Gaussian dynamic models. It includes in a general framework numerous methods proposed independently in various areas of science and proposes some original developments. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #561593. 	[Title] FILTERING VIA SIMULATION: AUXILIARY PARTICLE FILTERS [Abstract] This paper analyses the recently suggested particle approach to filtering time series. We suggest that the algorithm is not robust to outliers for two reasons: the design of the simulators and the use of the discrete support to represent the sequentially updating prior distribution. Both problems are tackled in this paper. We believe we have largely solved the first problem and have reduced the order of magnitude of the second. In addition we introduce the idea of stratification into the particle filter which allows us to perform on-line Bayesian calculations about the parameters which index the models and maximum likelihood estimation. The new methods are illustrated by using a stochastic volatility model and a time series model of angles. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #645084. 	[Title] Modelling Risk from a Disease in Time and Space [Abstract] This paper combines existing models for longitudinal and spatial data in a hierarchical Bayesian framework, with particular emphasis on the role of time- and space-varying covariate effects. Data analysis is implemented via Markov chain Monte Carlo methods. The methodology is illustrated by a tentative re-analysis of Ohio lung cancer data 1968-88. Two approaches that adjust for unmeasured spatial covariates, particularly tobacco consumption, are described. The first includes random effects in the model to account for unobserved heterogeneity; the second adds a simple urbanization measure as a surrogate for smoking behaviour. The Ohio dataset has been of particular interest because of the suggestion that a nuclear facility in the southwest of the state may have caused increased levels of lung cancer there. However, we contend here that the data are inadequate for a proper investigation of this issue. fl Email: leo@stat.uni-muenchen.de Please summarise all the given information.

Paper #561595 has the title `Exploratory Modelling of Multiple Non-Stationary Time Series: Latent Process Structure and Decompositions  
` and the abstract `We describe and illustrate Bayesian approaches to modelling and analysis of multiple non-stationary time series. This begins with uni-variate models for collections of related time series assumedly driven by underlying but unobservable processes, referred to as dynamic latent factor processes. We focus on models in which the factor processes, and hence the observed time series, are modelled by time-varying autoregressions capable of flexibly representing ranges of observed non-stationary characteristics. We highlight concepts and new methods of time series decomposition to infer characteristics of latent components in time series, and relate uni-variate decomposition analyses to underlying multivariate dynamic factor structure. Our motivating application is in analysis of multiple EEG traces from an ongoing EEG study at Duke. In this study, individuals undergoing ECT therapy generate multiple EEG traces at various scalp locations, and physiological interest lies in identifying dependencies and dissimilarities across series. In addition to the multivariate and non-stationary aspects of the series, this area provides illustration of the new results about decomposition of time series into latent, physically interpretable components; this is illustrated in data analysis of one EEG data set. The paper also discusses current and future research directions. fl This research was supported in part by the National Science Foundation under grant DMS-9311071. The EEG data and context arose from discussions with Dr Andrew Krystal, of Duke University Medical Center, with whom continued interactions have been most valuable. Address for correspondence: Institute of Statistics and Decision Sciences, Duke University, Durham, NC 27708-0251 U.S.A. (http://www.stat.duke.edu) 
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Question: Which category should Paper #561595 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #561595 has the title `Exploratory Modelling of Multiple Non-Stationary Time Series: Latent Process Structure and Decompositions  
` and the abstract `We describe and illustrate Bayesian approaches to modelling and analysis of multiple non-stationary time series. This begins with uni-variate models for collections of related time series assumedly driven by underlying but unobservable processes, referred to as dynamic latent factor processes. We focus on models in which the factor processes, and hence the observed time series, are modelled by time-varying autoregressions capable of flexibly representing ranges of observed non-stationary characteristics. We highlight concepts and new methods of time series decomposition to infer characteristics of latent components in time series, and relate uni-variate decomposition analyses to underlying multivariate dynamic factor structure. Our motivating application is in analysis of multiple EEG traces from an ongoing EEG study at Duke. In this study, individuals undergoing ECT therapy generate multiple EEG traces at various scalp locations, and physiological interest lies in identifying dependencies and dissimilarities across series. In addition to the multivariate and non-stationary aspects of the series, this area provides illustration of the new results about decomposition of time series into latent, physically interpretable components; this is illustrated in data analysis of one EEG data set. The paper also discusses current and future research directions. fl This research was supported in part by the National Science Foundation under grant DMS-9311071. The EEG data and context arose from discussions with Dr Andrew Krystal, of Duke University Medical Center, with whom continued interactions have been most valuable. Address for correspondence: Institute of Statistics and Decision Sciences, Duke University, Durham, NC 27708-0251 U.S.A. (http://www.stat.duke.edu) 
`.
Question: Which category should Paper #561595 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