Nonparametric Bayesian Discrete Latent Variable Models For Unsupervised Learning
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Nonparametric Bayesian Discrete Latent Variable Models For Unsupervised Learning
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Author :
language : en
Publisher:
Release Date : 2007
Nonparametric Bayesian Discrete Latent Variable Models For Unsupervised Learning written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with categories.
Bayesian Nonparametric Methods For Non Exchangeable Data
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Author : Nicholas J. Foti
language : en
Publisher:
Release Date : 2013
Bayesian Nonparametric Methods For Non Exchangeable Data written by Nicholas J. Foti and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with categories.
Bayesian nonparametric methods have become increasingly popular in machine learning for their ability to allow the data to determine model complexity. In particular, Bayesian nonparametric versions of common latent variable models can learn as effective dimension of the latent space. Examples include mixture models, latent feature models and topic models, where the number of components, features, or topics need not be specified a priori. A drawback of many of these models is that they assume the observations are exchangeable, that is, any dependencies between observations are ignored. This thesis contributes general methods to incorporate covariates into Bayesian nonparametric models and inference algorithms to learn with these models. First, we will present a flexible class of dependent Bayesian nonparametric priors to induce covariate-dependence into a variety of latent variable models used in machine learning. The proposed framework has nice analytic properites and admits a simple inference algorithm. We show how the framework can be used to construct a covariate-dependent latent feature model and a time-varying topic model. Second, we describe the first general purpose inference algorithm for a large family of dependent mixture models. Using the idea of slice-sampling, the algorithm is truncation-free and fast, showing that inference can de done efficiently despite the added complexity that covariate-dependence entails. Last, we construct a Bayesian nonparametric framework to couple multiple latent variable models and apply the framework to learning from multiple views of data.
A Deterministic Inference Framework For Discrete Nonparametric Latent Variable Models
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Author : Yordan Raykov
language : en
Publisher:
Release Date : 2017
A Deterministic Inference Framework For Discrete Nonparametric Latent Variable Models written by Yordan Raykov and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
Bayesian Nonparametrics
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Author : J.K. Ghosh
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-05-11
Bayesian Nonparametrics written by J.K. Ghosh and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-05-11 with Mathematics categories.
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.
Small Variance Asymptotics For Bayesian Models
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Author : Ke Jiang
language : en
Publisher:
Release Date : 2017
Small Variance Asymptotics For Bayesian Models written by Ke Jiang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
Bayesian models have been used extensively in various machine learning tasks, often resulting in improved prediction performance through the utilization of (layers of) latent variables when modeling the generative process of the observed data. Extending the parameter space from finite to infinite-dimensional, Bayesian nonparametric models can infer the model complexity directly from the data and thus also adapt with the amount of the observed data. This is especially appealing in the age of big data. However, such benefits come at a price: the parameter training and the prediction are notoriously difficult even for parametric models. Sampling and variational inference techniques are two standard methods for inference in Bayesian models, but for many problems, neither approach scales effectively to large-scale data. Currently, there is significant ongoing research trying to scale these methods using ideas from stochastic differential equations and stochastic optimization. A recent thread of research has considered small-variance asymptotics of latent-variable models as a way to capture the benefits of rich probabilistic models while also providing a framework for designing more scalable combinatorial optimization algorithms. Such models are often motivated by the well-known connection between mixtures of Gaussians and K-means: as the variances of the Gaussians tend to zero, the mixture of Gaussians model approaches K-means, both in terms of objectives and algorithms. In this dissertation, we will study small-variance asymptotics of Bayesian models, yielding new formulations and algorithms which may provide more efficient solutions to various unsupervised learning problems. Firstly, we consider clustering problems: exploring small-variance asymptotics for exponential family Dirichlet process (DP) and hierarchical Dirichlet process (HDP) mixture models. Utilizing connections between exponential family distributions and Bregman divergences, we derive novel clustering algorithms from the asymptotic limit of the DP and HDP mixtures that features the scalability of existing hard clustering methods as well as the flexibility of Bayesian nonparametric models. Secondly, we consider sequential models: exploring the small-variance asymptotic analysis of the infinite hidden Markov models, yielding a combinatorial objective function for discrete-data sequence observations with a non-fixed number of states. This involves a k-means-like term along with penalties based on state transitions and the number of states. We also present a simple, scalable, and flexible algorithm to optimize it. Lastly, we consider the topic modeling problems, which have emerged as fundamental tools in unsupervised machine learning. We approach it via combinatorial optimization, and take a small-variance limit of the latent Dirichlet allocation model to derive a new objective function. We minimize this objective by using ideas from combinatorial optimization, obtaining a new, fast, and high-quality topic modeling algorithm. In particular, we show that our results are not only significantly better than traditional small-variance asymptotic based algorithms, but also truly competitive with popular probabilistic approaches.
Bayesian Nonparametrics Via Neural Networks
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Author : Herbert K. H. Lee
language : en
Publisher: SIAM
Release Date : 2004-01-01
Bayesian Nonparametrics Via Neural Networks written by Herbert K. H. Lee and has been published by SIAM this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-01-01 with Mathematics categories.
Bayesian Nonparametrics via Neural Networks is the first book to focus on neural networks in the context of nonparametric regression and classification, working within the Bayesian paradigm. Its goal is to demystify neural networks, putting them firmly in a statistical context rather than treating them as a black box. This approach is in contrast to existing books, which tend to treat neural networks as a machine learning algorithm instead of a statistical model. Once this underlying statistical model is recognized, other standard statistical techniques can be applied to improve the model. The Bayesian approach allows better accounting for uncertainty. This book covers uncertainty in model choice and methods to deal with this issue, exploring a number of ideas from statistics and machine learning. A detailed discussion on the choice of prior and new noninformative priors is included, along with a substantial literature review. Written for statisticians using statistical terminology, Bayesian Nonparametrics via Neural Networks will lead statisticians to an increased understanding of the neural network model and its applicability to real-world problems.
Bayesian Analysis In Natural Language Processing Second Edition
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Author : Shay Cohen
language : en
Publisher: Springer Nature
Release Date : 2022-05-31
Bayesian Analysis In Natural Language Processing Second Edition written by Shay Cohen and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-31 with Computers categories.
Natural language processing (NLP) went through a profound transformation in the mid-1980s when it shifted to make heavy use of corpora and data-driven techniques to analyze language. Since then, the use of statistical techniques in NLP has evolved in several ways. One such example of evolution took place in the late 1990s or early 2000s, when full-fledged Bayesian machinery was introduced to NLP. This Bayesian approach to NLP has come to accommodate various shortcomings in the frequentist approach and to enrich it, especially in the unsupervised setting, where statistical learning is done without target prediction examples. In this book, we cover the methods and algorithms that are needed to fluently read Bayesian learning papers in NLP and to do research in the area. These methods and algorithms are partially borrowed from both machine learning and statistics and are partially developed "in-house" in NLP. We cover inference techniques such as Markov chain Monte Carlo sampling and variational inference, Bayesian estimation, and nonparametric modeling. In response to rapid changes in the field, this second edition of the book includes a new chapter on representation learning and neural networks in the Bayesian context. We also cover fundamental concepts in Bayesian statistics such as prior distributions, conjugacy, and generative modeling. Finally, we review some of the fundamental modeling techniques in NLP, such as grammar modeling, neural networks and representation learning, and their use with Bayesian analysis.
Advanced Analytics And Learning On Temporal Data
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Author : Vincent Lemaire
language : en
Publisher: Springer Nature
Release Date : 2021-12-02
Advanced Analytics And Learning On Temporal Data written by Vincent Lemaire and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-02 with Computers categories.
This book constitutes the refereed proceedings of the 6th ECML PKDD Workshop on Advanced Analytics and Learning on Temporal Data, AALTD 2021, held during September 13-17, 2021. The workshop was planned to take place in Bilbao, Spain, but was held virtually due to the COVID-19 pandemic. The 12 full papers presented in this book were carefully reviewed and selected from 21 submissions. They focus on the following topics: Temporal Data Clustering; Classification of Univariate and Multivariate Time Series; Multivariate Time Series Co-clustering; Efficient Event Detection; Modeling Temporal Dependencies; Advanced Forecasting and Prediction Models; Cluster-based Forecasting; Explanation Methods for Time Series Classification; Multimodal Meta-Learning for Time Series Regression; and Multivariate Time Series Anomaly Detection.
Independent Random Sampling Methods
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Author : Luca Martino
language : en
Publisher: Springer
Release Date : 2018-03-31
Independent Random Sampling Methods written by Luca Martino and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-31 with Computers categories.
This book systematically addresses the design and analysis of efficient techniques for independent random sampling. Both general-purpose approaches, which can be used to generate samples from arbitrary probability distributions, and tailored techniques, designed to efficiently address common real-world practical problems, are introduced and discussed in detail. In turn, the monograph presents fundamental results and methodologies in the field, elaborating and developing them into the latest techniques. The theory and methods are illustrated with a varied collection of examples, which are discussed in detail in the text and supplemented with ready-to-run computer code. The main problem addressed in the book is how to generate independent random samples from an arbitrary probability distribution with the weakest possible constraints or assumptions in a form suitable for practical implementation. The authors review the fundamental results and methods in the field, address the latest methods, and emphasize the links and interplay between ostensibly diverse techniques.
Nonparametric Bayesian Models For Machine Learning
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Author : Romain Jean Thibaux
language : en
Publisher:
Release Date : 2008
Nonparametric Bayesian Models For Machine Learning written by Romain Jean Thibaux and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with categories.