Download or read book Nonparametric Hierarchical Bayesian Models of Categorization written by Kevin Canini and published by . This book was released on 2011 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: Categorization, or classification, is a fundamental problem in both cognitive psychology and machine learning. Classical psychological models of categorization fall into two main groups: prototype models and exemplar models, which are equivalent, respectively, to the statistical methods of parametric density estimation and kernel density estimation. Many categorization studies in psychology attempt to understand how people solve this problem by comparing their inferences to those of formal computational models such as prototype or exemplar models. From this perspective, different models make different predictions about the representations and mechanisms people use to make categorization judgments. Instead, one can seek to understand categorization by viewing it as a problem of statistical inference and attempting to characterize the inductive biases of human learners. These inductive biases can be directly exposed using an experimental method called iterated learning, which provides direct insight into human categorization in a way that is independent of any proposed models. I describe the results of an iterated learning study of human categorization which supports previous findings by psychologists that people's representations seem to be more flexible than would be implied by either prototype or exemplar models alone. Prototype and exemplar models both use a single, fixed level of complexity in their representations of categories, with prototype models exhibiting the simplest representations, and exemplar models using the most complex representations. Treating categorization as a type of statistical inference, I describe a family of nonparametric Bayesian models of categorization based on the Dirichlet process mixture model (DPMM). These models represent categories as combinations of clusters of objects and, together, produce a continuum of representational complexities where prototype and exemplar models are special cases, occupying opposite ends of the spectrum. DPMM models allow the level of complexity of category representations to be chosen to suit the task at hand or to change over time; this flexibility can explain psychological results demonstrating that people's inferences are more congruent with prototype models at some times and exemplar models at other times. The DPMM can be generalized into a larger framework of models based on the hierarchical Dirichlet process (HDP). The HDP subsumes the DPMM and multiple previous psychological models, including prototypes, exemplars, and the Rational Model of Categorization. In addition, the HDP contains a family of previously unexplored models which make interesting predictions about how information can be shared between multiple categories. While most other categorization models learn each individual category in isolation and independently of the others, these HDP models share information between categories. This sharing of information can improve the speed and accuracy of learning and explained certain transfer learning effects that were observed in people's judgments. I introduce an extension of the HDP, called the tree-HDP, which is designed to infer systems of hierarchically related categories. The tree-HDP is able to simultaneously learn categories at multiple levels of generality and infer the taxonomic relationships between them. The original scientific contributions of this dissertation are a detailed characterization of the inductive biases of human categorization via iterated learning, a unification of previous psychological models of categorization into a common Bayesian statistical framework (the HDP), a demonstration that this framework contains interesting and previously unexplored models that predict and explain the integration of information from multiple categories, and a proposal and exploration of a new statistical model, the tree-HDP, which can simultaneously learn categories at multiple hierarchical levels and infer taxonomic relationships between those categories.
Download or read book Bayesian Nonparametrics written by Nils Lid Hjort and published by Cambridge University Press. This book was released on 2010-04-12 with total page 309 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian nonparametrics works - theoretically, computationally. The theory provides highly flexible models whose complexity grows appropriately with the amount of data. Computational issues, though challenging, are no longer intractable. All that is needed is an entry point: this intelligent book is the perfect guide to what can seem a forbidding landscape. Tutorial chapters by Ghosal, Lijoi and Prünster, Teh and Jordan, and Dunson advance from theory, to basic models and hierarchical modeling, to applications and implementation, particularly in computer science and biostatistics. These are complemented by companion chapters by the editors and Griffin and Quintana, providing additional models, examining computational issues, identifying future growth areas, and giving links to related topics. This coherent text gives ready access both to underlying principles and to state-of-the-art practice. Specific examples are drawn from information retrieval, NLP, machine vision, computational biology, biostatistics, and bioinformatics.
Download or read book Bayesian Nonparametrics written by J.K. Ghosh and published by Springer Science & Business Media. This book was released on 2006-05-11 with total page 311 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is the first systematic treatment of Bayesian nonparametric methods and the theory behind them. It will also appeal to statisticians in general. The book is primarily aimed at graduate students and can be used as the text for a graduate course in Bayesian non-parametrics.
Download or read book Computing Bayesian Nonparametric Hierarchical Models written by Michael D. Escobar and published by . This book was released on 1992 with total page 14 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Bayesian Nonparametric Models for Name Disambiguation and Supervised Learning written by Andrew Mingbo Dai and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis presents new Bayesian nonparametric models and approaches for their development, for the problems of name disambiguation and supervised learning. Bayesian nonparametric methods form an increasingly popular approach for solving problems that demand a high amount of model flexibility. However, this field is relatively new, and there are many areas that need further investigation. Previous work on Bayesian nonparametrics has neither fully explored the problems of entity disambiguation and supervised learning nor the advantages of nested hierarchical models. Entity disambiguation is a widely encountered problem where different references need to be linked to a real underlying entity. This problem is often unsupervised as there is no previously known information about the entities. Further to this, effective use of Bayesian nonparametrics offer a new approach to tackling supervised problems, which are frequently encountered. The main original contribution of this thesis is a set of new structured Dirichlet process mixture models for name disambiguation and supervised learning that can also have a wide range of applications. These models use techniques from Bayesian statistics, including hierarchical and nested Dirichlet processes, generalised linear models, Markov chain Monte Carlo methods and optimisation techniques such as BFGS. The new models have tangible advantages over existing methods in the field as shown with experiments on real-world datasets including citation databases and classification and regression datasets. I develop the unsupervised author-topic space model for author disambiguation that uses free-text to perform disambiguation unlike traditional author disambiguation approaches. The model incorporates a name variant model that is based on a nonparametric Dirichlet language model. The model handles both novel unseen name variants and can model the unknown authors of the text of the documents. Through this, the model can disambiguate authors with no prior knowledge of the number of true authors in the dataset. In addition, it can do this when the authors have identical names. I use a model for nesting Dirichlet processes named the hybrid NDP-HDP. This model allows Dirichlet processes to be clustered together and adds an additional level of structure to the hierarchical Dirichlet process. I also develop a new hierarchical extension to the hybrid NDP-HDP. I develop this model into the grouped author-topic model for the entity disambiguation task. The grouped author-topic model uses clusters to model the co-occurrence of entities in documents, which can be interpreted as research groups. Since this model does not require entities to be linked to specific words in a document, it overcomes the problems of some existing author-topic models. The model incorporates a new method for modelling name variants, so that domain-specific name variant models can be used. Lastly, I develop extensions to supervised latent Dirichlet allocation, a type of supervised topic model. The keyword-supervised LDA model predicts document responses more accurately by modelling the effect of individual words and their contexts directly. The supervised HDP model has more model flexibility by using Bayesian nonparametrics for supervised learning. These models are evaluated on a number of classification and regression problems, and the results show that they outperform existing supervised topic modelling approaches. The models can also be extended to use similar information to the previous models, incorporating additional information such as entities and document titles to improve prediction.
Download or read book On Nonparametric Bayesian Hierarchical Modelling written by Liping Liu and published by . This book was released on 1996 with total page 96 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Bayesian Methods for Nonlinear Classification and Regression written by David G. T. Denison and published by John Wiley & Sons. This book was released on 2002-05-06 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bei der Regressionsanalyse von Datenmaterial erhält man leider selten lineare oder andere einfache Zusammenhänge (parametrische Modelle). Dieses Buch hilft Ihnen, auch komplexere, nichtparametrische Modelle zu verstehen und zu beherrschen. Stärken und Schwächen jedes einzelnen Modells werden durch die Anwendung auf Standarddatensätze demonstriert. Verbreitete nichtparametrische Modelle werden mit Hilfe von Bayes-Verfahren in einen kohärenten wahrscheinlichkeitstheoretischen Zusammenhang gebracht.
Download or read book Hierarchical Bayesian Nonparametric Models for Power law Sequences written by Jan Alexander Gasthaus and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Nonparametric Bayesian Methods for Evaluating Fit in Hierarchical Models written by Kert Viele and published by . This book was released on 1996 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Bayesian Nonparametrics via Neural Networks written by Herbert K. H. Lee and published by SIAM. This book was released on 2004-06-01 with total page 103 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the first book to discuss neural networks in a nonparametric regression and classification context, within the Bayesian paradigm.
Download or read book Bayesian and Nonparametric Models in the Classification Problem written by Craig Allen Cooley and published by . This book was released on 1996 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Bayesian Non parametric Models and Inference for Sparse and Hierarchical Latent Structure written by David Arthur Knowles and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Nonlinear Mixture Models A Bayesian Approach written by Tatiana V Tatarinova and published by World Scientific. This book was released on 2014-12-30 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book, written by two mathematicians from the University of Southern California, provides a broad introduction to the important subject of nonlinear mixture models from a Bayesian perspective. It contains background material, a brief description of Markov chain theory, as well as novel algorithms and their applications. It is self-contained and unified in presentation, which makes it ideal for use as an advanced textbook by graduate students and as a reference for independent researchers. The explanations in the book are detailed enough to capture the interest of the curious reader, and complete enough to provide the necessary background material needed to go further into the subject and explore the research literature.In this book the authors present Bayesian methods of analysis for nonlinear, hierarchical mixture models, with a finite, but possibly unknown, number of components. These methods are then applied to various problems including population pharmacokinetics and gene expression analysis. In population pharmacokinetics, the nonlinear mixture model, based on previous clinical data, becomes the prior distribution for individual therapy. For gene expression data, one application included in the book is to determine which genes should be associated with the same component of the mixture (also known as a clustering problem). The book also contains examples of computer programs written in BUGS. This is the first book of its kind to cover many of the topics in this field.
Download or read book Networks of Mixture Blocks for Non Parametric Bayesian Models with Applications written by Ian Porteous and published by . This book was released on 2010 with total page 123 pages. Available in PDF, EPUB and Kindle. Book excerpt: This study brings together Bayesian networks, topic models, hierarchical Bayes modeling and nonparametric Bayesian methods to build a framework for efficiently designing and implementing a family of (non)parametric Bayesian mixture models. Bayesian mixture models, including Bayesian topic models, have shown themselves to be a useful tool for modeling and discovering latent structure in a number of domains. We introduce a modeling framework, networks of mixture blocks, that brings together these developments in a way that facilitates the definition and implementation of complex (non)parametric Bayesian networks for data with partitioned structure. Networks of mixture blocks can be viewed as Bayesian networks that have been factored into a network of sub-models, mixture blocks, which are conditionally independent of each other given the introduction of auxiliary partition variables. We use this framework to develop several novel nonparametric Bayesian models for collaborative filtering and text modeling.
Download or read book Hierarchical Bayesian Models for Linear and Non linear Animal Growth Curves written by and published by . This book was released on 2000 with total page 278 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Bayesian Nonparametric Models for Multi stage Sample Surveys written by Jiani Yin and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Nonparametric Bayesian Models for Machine Learning written by Romain Jean Thibaux and published by . This book was released on 2008 with total page 150 pages. Available in PDF, EPUB and Kindle. Book excerpt:
