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While it has long been taken for granted that context or background information plays a crucial role in reference assignment, there have been very few serious attempts to investigate exactly how they are used. This study provides an answer to the question through an extensive analysis of cases of bridging. The book demonstrates that when encountering a referring expression, the hearer is able to choose a set of contextual assumptions intended by the speaker in a principled way, out of all the assumptions possibly available to him. It claims more specifically that the use of context, as well as the assignment of referent, is governed by a single pragmatic principle, namely, the principle of relevance (Sperber & Wilson 1986/1995), which is also a single principle governing overall utterance interpretation. The explanatory power of the criterion based on the principle of relevance is tested against the two major, current alternatives - truth-based criteria and coherence-based criteria - using data elicited in a battery of referent assignment questionnaires. The results show clearly that the relevance-based criterion has more predictive power to handle a wider range of examples than any other existing criterion. As such, this work adds to the growing body of evidence supporting the insights of relevance theory.The work has been awarded the 2001 Ichikawa Award for the best achievement in English Linguistics by a young scholar in Japan.

Reference (Linguistics) --- Relevance. --- Pragmatics. --- Pragmalinguistics --- General semantics --- Language and languages --- Logic, Symbolic and mathematical --- Semantics (Philosophy) --- Pertinence --- Relevancy --- Meaning (Philosophy) --- Meaning (Psychology) --- Signification (Linguistics) --- Linguistics --- Onomasiology --- Semantics --- Philosophy --- 801.56 --- Syntaxis. Semantiek --- 801.56 Syntaxis. Semantiek --- Pragmatics

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This book provides a modern introductory tutorial on specialized methodological and applied aspects of spatial and temporal modeling. The areas covered involve a range of topics which reflect the diversity of this domain of research across a number of quantitative disciplines. For instance, the first chapter deals with non-parametric Bayesian inference via a recently developed framework known as kernel mean embedding which has had a significant influence in machine learning disciplines. The second chapter takes up non-parametric statistical methods for spatial field reconstruction and exceedance probability estimation based on Gaussian process-based models in the context of wireless sensor network data. The third chapter presents signal-processing methods applied to acoustic mood analysis based on music signal analysis. The fourth chapter covers models that are applicable to time series modeling in the domain of speech and language processing. This includes aspects of factor analysis, independent component analysis in an unsupervised learning setting. The chapter moves on to include more advanced topics on generalized latent variable topic models based on hierarchical Dirichlet processes which recently have been developed in non-parametric Bayesian literature. The final chapter discusses aspects of dependence modeling, primarily focusing on the role of extreme tail-dependence modeling, copulas, and their role in wireless communications system models.

Mathematical Statistics --- Mathematics --- Physical Sciences & Mathematics --- Spatial analysis (Statistics) --- Analysis, Spatial (Statistics) --- Statistics. --- Statistical Theory and Methods. --- Statistics and Computing/Statistics Programs. --- Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. --- Correlation (Statistics) --- Spatial systems --- Mathematical statistics. --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Econometrics --- Statistical inference --- Statistics, Mathematical --- Statistics --- Probabilities --- Sampling (Statistics) --- Statistics .

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This book provides a modern introductory tutorial on specialized theoretical aspects of spatial and temporal modeling. The areas covered involve a range of topics which reflect the diversity of this domain of research across a number of quantitative disciplines. For instance, the first chapter provides up-to-date coverage of particle association measures that underpin the theoretical properties of recently developed random set methods in space and time otherwise known as the class of probability hypothesis density framework (PHD filters). The second chapter gives an overview of recent advances in Monte Carlo methods for Bayesian filtering in high-dimensional spaces. In particular, the chapter explains how one may extend classical sequential Monte Carlo methods for filtering and static inference problems to high dimensions and big-data applications. The third chapter presents an overview of generalized families of processes that extend the class of Gaussian process models to heavy-tailed families known as alpha-stable processes. In particular, it covers aspects of characterization via the spectral measure of heavy-tailed distributions and then provides an overview of their applications in wireless communications channel modeling. The final chapter concludes with an overview of analysis for probabilistic spatial percolation methods that are relevant in the modeling of graphical networks and connectivity applications in sensor networks, which also incorporate stochastic geometry features.

Statistical science --- Computer. Automation --- informatica --- statistiek --- statistisch onderzoek