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Showing 1 - 12 of 12 results for “eric moulines”
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  • Markov Chains

    by Randal Douc, Eric Moulines, Pierre Priouret, Philippe Soulier ...
    Series series Mathematics and Statistics (R0)
    This book covers the classical theory of Markov chains on general state-spaces as well as many recent developments. The theoretical results are illustrated by simple examples, many of which are taken from Markov Chain Monte Carlo methods. The book is self-contained, while all the results are carefully and concisely proven. Bibliographical notes are added at the end of each chapter to provide an ... Read more

    $89.99 USD

  • Nonlinear Time Series

    Theory, Methods and Applications with R Examples

    by Randal Douc, Eric Moulines, David Stoffer ...
    Series series Chapman & Hall/CRC Texts in Statistical Science
    This text emphasizes nonlinear models for a course in time series analysis. After introducing stochastic processes, Markov chains, Poisson processes, and ARMA models, the authors cover functional autoregressive, ARCH, threshold AR, and discrete time series models as well as several complementary approaches. They discuss the main limit theorems for Markov chains, useful inequalities, statistical ... Read more

    $171.99 USD

  • Foundations of Modern Statistics

    Festschrift in Honor of Vladimir Spokoiny, Berlin, Germany, November 6–8, 2019, Moscow, Russia, November 30, 2019

    Edited by Denis Belomestny, Cristina Butucea, Enno Mammen, Eric Moulines, Markus Reiß, Vladimir V. Ulyanov ...
    Series series Springer Nature Proceedings excluding Computer Science
    This book contains contributions from the participants of the international conference “Foundations of Modern Statistics” which took place at Weierstrass Institute for Applied Analysis and Stochastics (WIAS), Berlin, during November 6–8, 2019, and at Higher School of Economics (HSE University), Moscow, during November 30, 2019. The events were organized in honor of Professor Vladimir Spokoiny on ... Read more

    $170.99 USD

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  • Data Assimilation Fundamentals

    A Unified Formulation of the State and Parameter Estimation Problem

    by Geir Evensen, Femke C. Vossepoel, Peter Jan van Leeuwen ...
    Series series Springer Textbooks in Earth Sciences, Geography and Environment
    This open-access textbook's significant contribution is the unified derivation of data-assimilation techniques from a common fundamental and optimal starting point, namely Bayes' theorem. Unique for this book is the "top-down" derivation of the assimilation methods. It starts from Bayes theorem and gradually introduces the assumptions and approximations needed to arrive at today's popular data ... Read more

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  • 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

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  • Probability, Random Processes, and Statistical Analysis

    Applications to Communications, Signal Processing, Queueing Theory and Mathematical Finance

    by Hisashi Kobayashi, Brian L. Mark, William Turin ...
    Together with the fundamentals of probability, random processes and statistical analysis, this insightful book also presents a broad range of advanced topics and applications. There is extensive coverage of Bayesian vs. frequentist statistics, time series and spectral representation, inequalities, bound and approximation, maximum-likelihood estimation and the expectation-maximization (EM) ... Read more

    $85.99 USD

  • Statistical Inference Based on the likelihood

    by Adelchi Azzalini ...
    Series series Chapman & Hall/CRC Monographs on Statistics and Applied Probability
    The Likelihood plays a key role in both introducing general notions of statistical theory, and in developing specific methods. This book introduces likelihood-based statistical theory and related methods from a classical viewpoint, and demonstrates how the main body of currently used statistical techniques can be generated from a few key concepts, in particular the likelihood. Focusing on those ... Read more

    $69.99 USD

  • Kernel-based Approximation Methods Using Matlab

    by Gregory E Fasshauer, Michael J Mccourt ...
    Series Book 19 - Interdisciplinary Mathematical Sciences
    In an attempt to introduce application scientists and graduate students to the exciting topic of positive definite kernels and radial basis functions, this book presents modern theoretical results on kernel-based approximation methods and demonstrates their implementation in various settings. The authors explore the historical context of this fascinating topic and explain recent advances as ... Read more

    $30.99 USD

  • Quantifying Life

    A Symbiosis of Computation, Mathematics, and Biology

    by Dmitry A. Kondrashov ...
    Since the time of Isaac Newton, physicists have used mathematics to describe the behavior of matter of all sizes, from subatomic particles to galaxies. In the past three decades, as advances in molecular biology have produced an avalanche of data, computational and mathematical techniques have also become necessary tools in the arsenal of biologists. But while quantitative approaches are now ... Read more

    $12.99 USD or Free with Kobo Plus

  • Linear Models and the Relevant Distributions and Matrix Algebra

    by David A. Harville ...
    Series series Chapman & Hall/CRC Texts in Statistical Science
    Linear Models and the Relevant Distributions and Matrix Algebra provides in-depth and detailed coverage of the use of linear statistical models as a basis for parametric and predictive inference. It can be a valuable reference, a primary or secondary text in a graduate-level course on linear models, or a resource used (in a course on mathematical statistics) to illustrate various theoretical ... Read more

    $64.99 USD

  • Variational Methods for Machine Learning with Applications to Deep Networks

    by Lucas Pinheiro Cinelli, Matheus Araújo Marins, Eduardo Antônio Barros da Silva, Sérgio Lima Netto ...
    Series series Engineering (R0)
    This book provides a straightforward look at the concepts, algorithms and advantages of Bayesian Deep Learning and Deep Generative Models. Starting from the model-based approach to Machine Learning, the authors motivate Probabilistic Graphical Models and show how Bayesian inference naturally lends itself to this framework. The authors present detailed explanations of the main modern algorithms on ... Read more

    $89.99 USD

  • Braverman Readings in Machine Learning. Key Ideas from Inception to Current State

    International Conference Commemorating the 40th Anniversary of Emmanuil Braverman's Decease, Boston, MA, USA, April 28-30, 2017, Invited Talks

    Edited by Lev Rozonoer, Boris Mirkin, Ilya Muchnik ...
    Series series Computer Science (R0)
    This state-of-the-art survey is dedicated to the memory of Emmanuil Markovich Braverman (1931-1977), a pioneer in developing machine learning theory.The 12 revised full papers and 4 short papers included in this volume were presented at the conference "Braverman Readings in Machine Learning: Key Ideas from Inception to Current State" held in Boston, MA, USA, in April 2017, commemorating the 40th ... Read more

    $49.99 USD