Linear and Parallel Learning of Markov Random Fields
Author | : Yariv Dror Mizrahi |
Publisher | : |
Total Pages | : |
Release | : 2014 |
Genre | : |
ISBN | : |
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Author | : Yariv Dror Mizrahi |
Publisher | : |
Total Pages | : |
Release | : 2014 |
Genre | : |
ISBN | : |
Author | : Y.A. Rozanov |
Publisher | : Springer Science & Business Media |
Total Pages | : 207 |
Release | : 2012-12-06 |
Genre | : Mathematics |
ISBN | : 1461381908 |
In this book we study Markov random functions of several variables. What is traditionally meant by the Markov property for a random process (a random function of one time variable) is connected to the concept of the phase state of the process and refers to the independence of the behavior of the process in the future from its behavior in the past, given knowledge of its state at the present moment. Extension to a generalized random process immediately raises nontrivial questions about the definition of a suitable" phase state," so that given the state, future behavior does not depend on past behavior. Attempts to translate the Markov property to random functions of multi-dimensional "time," where the role of "past" and "future" are taken by arbitrary complementary regions in an appro priate multi-dimensional time domain have, until comparatively recently, been carried out only in the framework of isolated examples. How the Markov property should be formulated for generalized random functions of several variables is the principal question in this book. We think that it has been substantially answered by recent results establishing the Markov property for a whole collection of different classes of random functions. These results are interesting for their applications as well as for the theory. In establishing them, we found it useful to introduce a general probability model which we have called a random field. In this book we investigate random fields on continuous time domains. Contents CHAPTER 1 General Facts About Probability Distributions §1.
Author | : Ponnuswamy Sadayappan |
Publisher | : Springer Nature |
Total Pages | : 564 |
Release | : 2020-06-15 |
Genre | : Computers |
ISBN | : 3030507432 |
This book constitutes the refereed proceedings of the 35th International Conference on High Performance Computing, ISC High Performance 2020, held in Frankfurt/Main, Germany, in June 2020.* The 27 revised full papers presented were carefully reviewed and selected from 87 submissions. The papers cover a broad range of topics such as architectures, networks & infrastructure; artificial intelligence and machine learning; data, storage & visualization; emerging technologies; HPC algorithms; HPC applications; performance modeling & measurement; programming models & systems software. *The conference was held virtually due to the COVID-19 pandemic. Chapters "Scalable Hierarchical Aggregation and Reduction Protocol (SHARP) Streaming-Aggregation Hardware Design and Evaluation", "Solving Acoustic Boundary Integral Equations Using High Performance Tile Low-Rank LU Factorization", "Scaling Genomics Data Processing with Memory-Driven Computing to Accelerate Computational Biology", "Footprint-Aware Power Capping for Hybrid Memory Based Systems", and "Pattern-Aware Staging for Hybrid Memory Systems" are available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
Author | : Ross Kindermann |
Publisher | : |
Total Pages | : 160 |
Release | : 1980 |
Genre | : Mathematics |
ISBN | : |
The study of Markov random fields has brought exciting new problems to probability theory which are being developed in parallel with basic investigation in other disciplines, most notably physics. The mathematical and physical literature is often quite technical. This book aims at a more gentle introduction to these new areas of research.
Author | : Havard Rue |
Publisher | : CRC Press |
Total Pages | : 280 |
Release | : 2005-02-18 |
Genre | : Mathematics |
ISBN | : 0203492021 |
Gaussian Markov Random Field (GMRF) models are most widely used in spatial statistics - a very active area of research in which few up-to-date reference works are available. This is the first book on the subject that provides a unified framework of GMRFs with particular emphasis on the computational aspects. This book includes extensive case-studie
Author | : Rama Chellappa |
Publisher | : |
Total Pages | : 608 |
Release | : 1993 |
Genre | : Mathematics |
ISBN | : |
Introduces the theory and application of Markov random fields in image processing/computer vision. Modelling images through the local interaction of Markov models produces algorithms for use in texture analysis, image synthesis, restoration, segmentation and surface reconstruction.
Author | : Abhin Swapnil Shah |
Publisher | : |
Total Pages | : 128 |
Release | : 2021 |
Genre | : |
ISBN | : |
We consider learning a sparse pairwise Markov Random Field with continuous valued variables from i.i.d samples. We adapt the framework of generalized interaction screening objective to this setting and provide finite-sample analysis revealing sample complexity scaling logarithmically with the number of variables, as in the discrete and Gaussian settings. Our approach is applicable to a large class of pairwise Markov Random Fields with continuous variables and also has desirable asymptotic properties, including consistency and normality under mild conditions. Further, we establish that the population version of generalized interaction screening objective can be interpreted as local maximum likelihood estimation. As part of our analysis, we introduce a robust variation of sparse linear regression à la Lasso, which may be of interest in its own right.
Author | : M. J. Swain |
Publisher | : |
Total Pages | : 34 |
Release | : 1990 |
Genre | : Machine learning |
ISBN | : |
Abstract: "We study the problem of learning parameters of a Markov Random Field (MRF) from observations and propose two new approaches suitable for use with Highest Confidence First (HCF) estimation. Both approaches involve estimating local joint probabilities from experience. In one approach the joint probabilities are converted to clique parameters of the Gibbs distribution so that the traditional HCF algorithm can be used. In the other approach the HCF algorithm is modified to run directly with the local probabilities of the MRF instead of the Gibbs distribution."
Author | : Arun Kumar Sangaiah |
Publisher | : Academic Press |
Total Pages | : 280 |
Release | : 2019-07-26 |
Genre | : Computers |
ISBN | : 0128172932 |
Deep Learning and Parallel Computing Environment for Bioengineering Systems delivers a significant forum for the technical advancement of deep learning in parallel computing environment across bio-engineering diversified domains and its applications. Pursuing an interdisciplinary approach, it focuses on methods used to identify and acquire valid, potentially useful knowledge sources. Managing the gathered knowledge and applying it to multiple domains including health care, social networks, mining, recommendation systems, image processing, pattern recognition and predictions using deep learning paradigms is the major strength of this book. This book integrates the core ideas of deep learning and its applications in bio engineering application domains, to be accessible to all scholars and academicians. The proposed techniques and concepts in this book can be extended in future to accommodate changing business organizations’ needs as well as practitioners’ innovative ideas. Presents novel, in-depth research contributions from a methodological/application perspective in understanding the fusion of deep machine learning paradigms and their capabilities in solving a diverse range of problems Illustrates the state-of-the-art and recent developments in the new theories and applications of deep learning approaches applied to parallel computing environment in bioengineering systems Provides concepts and technologies that are successfully used in the implementation of today's intelligent data-centric critical systems and multi-media Cloud-Big data
Author | : Charles Sutton |
Publisher | : Now Pub |
Total Pages | : 120 |
Release | : 2012 |
Genre | : Computers |
ISBN | : 9781601985729 |
An Introduction to Conditional Random Fields provides a comprehensive tutorial aimed at application-oriented practitioners seeking to apply CRFs. The monograph does not assume previous knowledge of graphical modeling, and so is intended to be useful to practitioners in a wide variety of fields.