The R2WinBUGS package provides convenient functions to call WinBUGS from R. It automatically writes the data and scripts in a format readable by WinBUGS for processing in batch mode, which is possible since version 1.4. After the WinBUGS process has finished, it is possible either to read the resulting data into R by the package itself—which gives a compact graphical summary of inference and convergence diagnostics—or to use the facilities of the coda package for further analyses of the output. Examples are given to demonstrate the usage of this package. Keywords: R, WinBUGS, interface, MCMC. An earlier version of this vignette has been published by the Journal of Statistical Software: Sturtz S, Ligges U, Gelman A (2005): “R2WinBUGS: A Package for Running WinBUGS from R.”

Summary. In problems with missing or latent data, a standard approach is to first impute the unobserved data, then perform all statistical analyses on the completed dataset—corresponding to the observed data and imputed unobserved data—using standard procedures for complete-data inference. Here, we extend this approach to model checking by demonstrating the advantages of the use of completed-data model diagnostics on imputed completed datasets. The approach is set in the theoretical framework of Bayesian posterior predictive checks (but, as with missing-data imputation, our methods of missing-data model checking can also be interpreted as “predictive inference ” in a non-Bayesian context). We consider the graphical diagnostics within this framework. Advantages of the completed-data approach include: (1) One can often check model fit in terms of quantities that are of key substantive interest in a natural way, which is not always possible using observed data alone. (2) In problems with missing data, checks may be devised that do not require to model the missingness or inclusion mechanism; the latter is useful for the analysis of ignorable but unknown data collection mechanisms, such as are often assumed in the analysis of sample surveys and observational studies. (3) In many problems with latent data, it is possible to check qualitative features of the model (for example, independence of two variables) that can be naturally formalized with the help of the latent data. We illustrate with several applied examples.

The Minimum Description Length (MDL) principle is an information theoretic approach to inductive inference that originated in algorithmic coding theory. In this approach, data are viewed as codes to be compressed by the model. From this perspective, models are compared on their ability to compress a data set by extracting useful information in the data apart from random noise. The goal of model selection is to identify the model, from a set of candidate models, that permits the shortest description length (code) of the data. Since Rissanen originally formalized the problem using the crude ‘two-part code ’ MDL method in the 1970s, many significant strides have been made, especially in the 1990s, with the culmination of the development of the refined ‘universal code’ MDL method, dubbed Normalized Maximum Likelihood (NML). It represents an elegant solution to the model selection problem. The present paper provides a tutorial review on these latest developments with a special focus on NML. An application example of NML in cognitive modeling is also provided.

This paper describes the construction and use of a novel representation for the recognition of objects and their parts, the semantic hierarchy. Its advantages include improved classification performance, accurate detection and localization of object parts and sub-parts, and explicitly identifying the different appearances of each object part. The semantic hierarchy algorithm starts by constructing a minimal feature hierarchy and proceeds by adding semantically equivalent representatives to each node, using the entire hierarchy as a context for determining the identity and locations of added features. Part detection is obtained by a bottom-up top-down cycle. Unlike previous approaches, the semantic hierarchy learns to represent the set of possible appearances of object parts at all levels, and their statistical dependencies. The algorithm is fully automatic and is shown experimentally to substantially improve the recognition of objects and their parts. 1.

Until recently there are no common, convenient, and repeatable evaluation methods that could be easily applied to support fast turn-around development of automatic text summarization systems. In this paper, we introduce an informationtheoretic approach to automatic evaluation of summaries based on the Jensen-Shannon divergence of distributions between an automatic summary and a set of reference summaries. Several variants of the approach are also considered and compared. The results indicate that JS divergencebased evaluation method achieves comparable performance with the common automatic evaluation method ROUGE in single documents summarization task; while achieves better performance than ROUGE in multiple document summarization task. 1

A major source of information (often the most crucial and informative part) in scholarly articles from scientific journals, proceedings and books are the figures that directly provide images and other graphical illustrations of key experimental results and other scientific contents. In biological articles, a typical figure often comprises multiple panels, accompanied by either scoped or global captioned text. Moreover, the text in the caption contains important semantic entities such as protein names, gene ontology, tissues labels, etc., relevant to the images in the figure. Due to the avalanche of biological literature in recent years, and increasing popularity of various bio-imaging techniques, automatic retrieval and summarization of biological information from literature figures has emerged as a major unsolved challenge in computational knowledge extraction and management in the life science. We present a new structured probabilistic topic model built on a realistic figure generation scheme to model the structurally annotated biological figures, and we derive an efficient inference algorithm based on collapsed Gibbs sampling for information retrieval and visualization. The resulting program constitutes one of the key IR engines in our SLIF system that has recently entered the final round (4 out 70 competing systems) of the Elsevier Grand Challenge on Knowledge Enhancement in the Life Science. Here we present various evaluations on a number of data

We present an interactive abstract painting system named Sisley. Sisley works upon the psychological principle [Berlyne 1971] that abstract arts are often characterized by their greater perceptual ambiguities than photographs, which tend to invoke moderate mental efforts of the audience for interpretation, accompanied with subtle aesthetic pleasures. Given an input photograph, Sisley decomposes it into a hierarchy/tree of its constituent image components (e.g., regions, objects of different categories) with interactive guidance painting images, with increased ambiguities of both the scene and individual objects at desired levels. Sisley consists of three major working parts: (1) an interactive image parser executing the tasks of segmentation, labeling, and hierarchical organization, (2) a painterly rendering engine with abstract operators for transferring the image appearance, and (3) a numerical ambiguity computation and control module of servomechanism. With the help of Sisley, even an amateur user can create abstract paintings from photographs easily in minutes. We have evaluated the rendering results of Sisley using human experiments, and verified that they have similar abstract effects to original abstract paintings by artists.

The deviance information criterion (DIC) is widely used for Bayesian model comparison, despite the lack of a clear theoretical foundation. DIC is shown to be an approximation to a penalized loss function based on the deviance, with a penalty derived from a cross-validation argument. This approximation is valid only when the effective number of parameters in the model is much smaller than the number of independent observations. In disease mapping, a typical application of DIC, this assumption does not hold and DIC under-penalizes more complex models. Another deviance-based loss function, derived from the same decision-theoretic framework, is applied to mixture models, which have previously been considered an unsuitable application for DIC.

Abstract not found

Abstract: Contemporary very-high-dimensional (VHD) statistical problems call at-tention more than ever to solving the fundamental problem of scientific inference, that is, to make situation-specific inference with credible evidential support. Af-ter scrutinizing the great innovative ideas behind Fisher’s fiducial argument and the Dempster-Shafer (DS) theory for scientific inference, we recognize that given a postulated sampling model, reasoning for statistical inference (about a particular realization of random variables) should be different from reasoning for data gener-ation. The classical belief in distributional invariance of pivotal variables does not distinguish these two types of reasoning processes and is thus often too strong to be believable. Intuitively, beliefs with higher credibility can be obtained from the classical belief by making it weaker. This general idea is termed as the “minimal belief ” (MB) principle. Technically, the proposed method is built on the DS the-ory, and provides ways to capture realistically more “don’t know ” and thereby to build better DS models for solving VHD problems. It is shown that for general single-parameter and certain multiparameter distributions, the MB posteriors are obtained in closed form. The method is illustrated with a variety of examples, in-cluding the simple test of significance, the Behrens-Fisher problem, the multinomial model, and the many-normal-means problem. The many-normal-means example of-fers an MB perspective of often-crude Bayesian and related shrinkage techniques, which have been considered necessary in the last half a century. Key words and phrases: Bayesian methods, Dempster-Shafer theory, fiducial infer-ence, Likelihood principle, Stein’s paradox. 1.