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 #67584. 	[Title] Hyperparameter estimation in Dirichlet process mixture models [Abstract] In Bayesian density estimation and prediction using Dirichlet process mixtures of standard, exponential family distributions, the precision or total mass parameter of the mixing Dirichlet process is a critical hyperparame-ter that strongly influences resulting inferences about numbers of mixture components. This note shows how, with respect to a flexible class of prior distributions for this parameter, the posterior may be represented in a simple conditional form that is easily simulated. As a result, inference about this key quantity may be developed in tandem with the existing, routine Gibbs sampling algorithms for fitting such mixture models. The concept of data augmentation is important, as ever, in developing this extension of the existing algorithm. A final section notes an simple asymptotic approx imation to the posterior. 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 #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 #1127566. 	[Title] Hierarchical priors and mixture models, with application in regression and density estimation [Abstract]  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 #561809. 	[Title] Bayesian curve fitting using multivariate normal mixtures [Abstract] Problems of regression smoothing and curve fitting are addressed via predictive inference in a flexible class of mixture models. Multi-dimensional density estimation using Dirichlet mixture models provides the theoretical basis for semi-parametric regression methods in which fitted regression functions may be deduced as means of conditional predictive distributions. These Bayesian regression functions have features similar to generalised kernel regression estimates, but the formal analysis addresses problems of multivariate smoothing parameter estimation and the assessment of uncertainties about regression functions naturally. Computations are based on multi-dimensional versions of existing Markov chain simulation analysis of univariate Dirichlet mixture models. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #562067. 	[Title] Computing Nonparametric Hierarchical Models [Abstract] Bayesian models involving Dirichlet process mixtures are at the heart of the modern nonparametric Bayesian movement. Much of the rapid development of these models in the last decade has been a direct result of advances in simulation-based computational methods. Some of the very early work in this area, circa 1988-1991, focused on the use of such nonparametric ideas and models in applications of otherwise standard hierarchical models. This chapter provides some historical review and perspective on these developments, with a prime focus on the use and integration of such nonparametric ideas in hierarchical models. We illustrate the ease with which the strict parametric assumptions common to most standard Bayesian hierarchical models can be relaxed to incorporate uncertainties about functional forms using Dirichlet process components, partly enabled by the approach to computation using MCMC methods. The resulting methology is illustrated with two examples taken from an unpublished 1992 report on the topic. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1127541. 	[Title] Mixture Models in the Exploration of Structure-Activity Relationships in Drug Design [Abstract] We report on a study of mixture modeling problems arising in the assessment of chemical structure-activity relationships in drug design and discovery. Pharmaceutical research laboratories developing test compounds for screening synthesize many related candidate compounds by linking together collections of basic molecular building blocks, known as monomers. These compounds are tested for biological activity, feeding in to screening for further analysis and drug design. The tests also provide data relating compound activity to chemical properties and aspects of the structure of associated monomers, and our focus here is studying such relationships as an aid to future monomer selection. The level of chemical activity of compounds is based on the geometry of chemical binding of test compounds to target binding sites on receptor compounds, but the screening tests are unable to identify binding configurations. Hence potentially critical covari-ate information is missing as a natural latent variable. Resulting statistical models are then mixed with respect to such missing information, so complicating data analysis and inference. This paper reports on a study of a two-monomer, two-binding site framework and associated data. We build structured mixture models that mix linear regression models, predicting chemical effectiveness, with respect to site-binding selection mechanisms. We discuss aspects of modeling and analysis, including problems and pitfalls, and describe results of analyses of a simulated and real data set. In modeling real data, we are led into critical model extensions that introduce hierarchical random effects components to adequately capture heterogeneities in both the site binding mechanisms and in the resulting levels of effectiveness of compounds once bound. Comments on current and potential future directions conclude the report. Please summarise all the given information.

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 #561789. 	[Title] Practical Bayesian Inference Using Mixtures of Mixtures [Abstract] Discrete mixtures of normal distributions are widely used in modeling amplitude fluctuations of electrical potentials at synapses of human, and other animal nervous systems. The usual framework has independent data values y j arising as y j = j + x n 0 +j where the means j come from some discrete prior G() and the unknown x n 0 +j 's and observed x j ; j = 1; : : : ; n 0 are gaussian noise terms. A practically important development of the associated statistical methods is the issue of non-normality of the noise terms, often the norm rather than the exception in the neurological context. We have recently developed models, based on convolutions of Dirichlet process mixtures, for such problems. Explicitly, we model the noise data values x j as arising from a Dirich-let process mixture of normals, in addition to modeling the location prior G() as a Dirichlet process itself. This induces a Dirichlet mixture of mixtures of normals, whose analysis may be developed using Gibbs sampling techniques. We discuss these models and their analysis, and illustrate in the context of neurological response analysis. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1127551. 	[Title] COMPUTING DISTRIBUTIONS OF ORDER STATISTICS [Abstract] Recurrence relationships among the distribution functions of order statistics of independent, but not identically distributed, random quantities are derived. These results extend known theory and provide computationally practicable algorithms for a variety of problems. Please summarise all the given information.

Paper #561674 has the title `Studies of Neurological Transmission Analysis using Hierarchical Bayesian Mixture Models  
` and the 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. 
`.
Question: Which category should Paper #561674 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #561674 has the title `Studies of Neurological Transmission Analysis using Hierarchical Bayesian Mixture Models  
` and the 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. 
`.
Question: Which category should Paper #561674 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: Neural_Networks