Monte Carlo Methods (蒙地卡羅方法)

Barbu, Adrian, Zhu, Song-Chun

Monte Carlo Methods
  • 出版商: Springer
  • 出版日期: 2020-02-25
  • 定價: $4,200
  • 售價: 8.0 折 $3,360
  • 語言: 英文
  • 頁數: 307
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 9811329702
  • ISBN-13: 9789811329708
  • 相關分類: Computer Vision

    • 立即出貨 (庫存 < 3)

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商品描述

This book seeks to bridge the gap between statistics and computer science. It provides an overview of Monte Carlo methods, including Sequential Monte Carlo, Markov Chain Monte Carlo, Metropolis-Hastings, Gibbs Sampler, Cluster Sampling, Data Driven MCMC, Stochastic Gradient descent, Langevin Monte Carlo, Hamiltonian Monte Carlo, and energy landscape mapping. Due to its comprehensive nature, the book is suitable for developing and teaching graduate courses on Monte Carlo methods. To facilitate learning, each chapter includes several representative application examples from various fields. The book pursues two main goals: (1) It introduces researchers to applying Monte Carlo methods to broader problems in areas such as Computer Vision, Computer Graphics, Machine Learning, Robotics, Artificial Intelligence, etc.; and (2) it makes it easier for scientists and engineers working in these areas to employ Monte Carlo methods to enhance their research.

商品描述(中文翻譯)

本書旨在彌補統計學和計算機科學之間的差距。它提供了蒙特卡羅方法的概述，包括順序蒙特卡羅、馬爾可夫鏈蒙特卡羅、Metropolis-Hastings、吉布斯抽樣、叢集抽樣、數據驅動的MCMC、隨機梯度下降、Langevin蒙特卡羅、Hamiltonian蒙特卡羅和能量景觀映射。由於其全面性，本書適合用於開發和教授蒙特卡羅方法的研究生課程。為了促進學習，每章都包含了來自不同領域的幾個代表性應用實例。本書追求兩個主要目標：（1）介紹研究人員將蒙特卡羅方法應用於計算機視覺、計算機圖形學、機器學習、機器人技術、人工智能等領域的更廣泛問題；（2）使在這些領域工作的科學家和工程師更容易利用蒙特卡羅方法來增強他們的研究。

作者簡介

Adrian Barbu received his PhD in Mathematics from Ohio State University in 2000 and his PhD in Computer Science from the University of California, Los Angeles in 2005. His research interests are in machine learning, computer vision and medical imaging. He received the 2011 Thomas A. Edison Patent Award with his co-authors from Siemens for their work on Marginal Space Learning. In 2007 he joined the Statistics Department at Florida State University, first as an assistant professor, and since 2013 as an associate professor.

Song-Chun Zhu received his PhD degree in Computer Science from Harvard University in 1996. He is currently a professor of Statistics and Computer Science, and director of the Center for Vision, Learning, Cognition and Autonomy, at the University of California, Los Angeles. His main research interest has been in pursuing a unified statistical and computational framework for vision and intelligence, which includes the Spatial, Temporal and Causal And-Or graph (STC-AOG) as a unified representation and numerous Monte Carlo methods for inference and learning. He has published over 200 papers in the areas of computer vision, statistical learning, cognition, AI, and robot autonomy. He has received a number of honors, including the David Marr Prize in 2003 for image parsing, and twice Marr Prize honorary nominations in 1999 for texture modeling and in 2007 for object modeling. In 2008 he received the J.K. Aggarwal Prize from the Intl. Association of Pattern Recognition for "contributions to a unified foundation for visual pattern conceptualization, modeling, learning, and inference". In 2013 he received the Helmholtz Test-of-Time Prize for a paper on image segmentation. He has been a fellow of IEEE Computer Society since 2011, and the principal investigator leading several ONR MURI and DARPA teams working on scene and event understanding and cognitive robots under a unified mathematical framework.

 

作者簡介(中文翻譯)

Adrian Barbu於2000年在俄亥俄州立大學獲得數學博士學位，並於2005年在加州大學洛杉磯分校獲得計算機科學博士學位。他的研究興趣包括機器學習、計算機視覺和醫學影像。他與西門子的合著者們因他們在邊緣空間學習方面的工作而獲得了2011年的湯瑪斯·愛迪生專利獎。2007年，他加入佛羅里達州立大學統計學系，起初擔任助理教授，自2013年起擔任副教授。

Song-Chun Zhu於1996年在哈佛大學獲得計算機科學博士學位。他目前是加州大學洛杉磯分校統計學和計算機科學教授，以及視覺、學習、認知和自主性中心的主任。他的主要研究興趣是追求視覺和智能的統一統計和計算框架，其中包括空間、時間和因果And-Or圖（STC-AOG）作為統一表示以及多種蒙特卡羅方法用於推理和學習。他在計算機視覺、統計學習、認知、人工智能和機器人自主性領域發表了200多篇論文。他獲得了多項榮譽，包括2003年的David Marr獎（用於圖像解析），以及1999年的兩次Marr獎提名（用於紋理建模）和2007年的一次物體建模提名。2008年，他因為對視覺模式概念化、建模、學習和推理的統一基礎的貢獻而獲得了國際模式識別協會的J.K. Aggarwal獎。2013年，他因一篇關於圖像分割的論文獲得了Helmholtz時間考驗獎。自2011年以來，他一直是IEEE計算機學會的會士，並且是數個ONR MURI和DARPA團隊的首席調查員，這些團隊在統一的數學框架下致力於場景和事件理解以及認知機器人的研究。

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