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Showing 1 - 12 of 12 results for “kai wang ng”
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  • Bayesian Missing Data Problems

    EM, Data Augmentation and Noniterative Computation

    by Ming T. Tan, Guo-Liang Tian, Kai Wang Ng ...
    Bayesian Missing Data Problems: EM, Data Augmentation and Noniterative Computation presents solutions to missing data problems through explicit or noniterative sampling calculation of Bayesian posteriors. The methods are based on the inverse Bayes formulae discovered by one of the author in 1995. Applying the Bayesian approach to important real-wor ... Read more

    $89.99 USD

  • Dirichlet and Related Distributions

    Theory, Methods and Applications

    by Kai Wang Ng, Guo-Liang Tian, Man-Lai Tang ...
    Series Book 888 - Wiley Series in Probability and Statistics
    The Dirichlet distribution appears in many areas of application, which include modelling of compositional data, Bayesian analysis, statistical genetics, and nonparametric inference. This book provides a comprehensive review of the Dirichlet distribution and two extended versions, the Grouped Dirichlet Distribution (GDD) and the Nested Dirichlet Distribution (NDD), arising from likelihood and ... Read more

    $125.75 USD

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  • Probabilistic Machine Learning

    An Introduction

    by Kevin P. Murphy ...
    Series series Adaptive Computation and Machine Learning series
    A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory.This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (including linear algebra ... Read more

    $77.99 USD

  • Vision

    A Computational Investigation into the Human Representation and Processing of Visual Information

    by David Marr ...
    Available again, an influential book that offers a framework for understanding visual perception and considers fundamental questions about the brain and its functions.David Marr's posthumously published Vision (1982) influenced a generation of brain and cognitive scientists, inspiring many to enter the field. In Vision, Marr describes a general framework for understanding visual perception and ... Read more

    $31.99 USD

  • Bayesian Statistics

    An Introduction

    by Peter M. Lee ...
    Bayesian Statistics is the school of thought that combines prior beliefs with the likelihood of a hypothesis to arrive at posterior beliefs. The first edition of Peter Lee’s book appeared in 1989, but the subject has moved ever onwards, with increasing emphasis on Monte Carlo based techniques.This new fourth edition looks at recent techniques such as variational methods, Bayesian importance ... Read more

    $58.00 USD

  • Generalized Additive Models

    An Introduction with R, Second Edition

    by Simon N. Wood ...
    Series series Chapman & Hall/CRC Texts in Statistical Science
    The first edition of this book has established itself as one of the leading references on generalized additive models (GAMs), and the only book on the topic to be introductory in nature with a wealth of practical examples and software implementation. It is self-contained, providing the necessary background in linear models, linear mixed models, and generalized linear models (GLMs), before ... Read more

    $120.99 USD

  • Regularized System Identification

    Learning Dynamic Models from Data

    by Gianluigi Pillonetto, Tianshi Chen, Alessandro Chiuso, Giuseppe De Nicolao, Lennart Ljung ...
    Series series Engineering (R0)
    This open access book provides a comprehensive treatment of recent developments in kernel-based identification that are of interest to anyone engaged in learning dynamic systems from data. The reader is led step by step into understanding of a novel paradigm that leverages the power of machine learning without losing sight of the system-theoretical principles of black-box identification. The ... Read more

    Free

  • Time Series Analysis and Its Applications

    With R Examples

    by Robert H. Shumway, David S. Stoffer ...
    Series series Mathematics and Statistics (R0)
    The fourth edition of this popular graduate textbook, like its predecessors, presents a balanced and comprehensive treatment of both time and frequency domain methods with accompanying theory. Numerous examples using nontrivial data illustrate solutions to problems such as discovering natural and anthropogenic climate change, evaluating pain perception experiments using functional magnetic ... Read more

    $89.09 USD

  • A First Course in Machine Learning

    by Simon Rogers, Mark Girolami ...
    Series series Chapman & Hall/CRC Machine Learning & Pattern Recognition
    "A First Course in Machine Learning by Simon Rogers and Mark Girolami is the best introductory book for ML currently available. It combines rigor and precision with accessibility, starts from a detailed explanation of the basic foundations of Bayesian analysis in the simplest of settings, and goes all the way to the frontiers of the subject such as infinite mixture models, GPs, and MCMC."—Devdatt ... Read more

    $61.99 USD

  • Bayesian Analysis of Stochastic Process Models

    by Mike Wiper, Fabrizio Ruggeri, David Insua ...
    Series Book 978 - Wiley Series in Probability and Statistics
    Bayesian analysis of complex models based on stochastic processes has in recent years become a growing area. This book provides a unified treatment of Bayesian analysis of models based on stochastic processes, covering the main classes of stochastic processing including modeling, computational, inference, forecasting, decision making and important applied models.Key features:Explores Bayesian ... Read more

    $101.00 USD

  • Bayesian Essentials with R

    by Christian P. Robert, Jean-Michel Marin ...
    Series series Mathematics and Statistics (R0)
    This Bayesian modeling book provides a self-contained entry to computational Bayesian statistics. Focusing on the most standard statistical models and backed up by real datasets and an all-inclusive R (CRAN) package called bayess, the book provides an operational methodology for conducting Bayesian inference, rather than focusing on its theoretical and philosophical justifications.Readers are ... Read more

    $98.09 USD

  • Probabilistic Machine Learning

    Advanced Topics

    by Kevin P. Murphy ...
    Series series Adaptive Computation and Machine Learning series
    An advanced book for researchers and graduate students working in machine learning and statistics who want to learn about deep learning, Bayesian inference, generative models, and decision making under uncertainty.An advanced counterpart to Probabilistic Machine Learning: An Introduction, this high-level textbook provides researchers and graduate students detailed coverage of cutting-edge topics ... Read more

    $92.99 USD