正版保障 假一赔十 可开发票
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作者:
(美)巴布·艾俊(Adrian Barbu)，朱松纯著
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出版社:
电子工业出版社
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ISBN:
9787121470202
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出版时间:
2024-01
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装帧:
平装
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开本:
其他
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作者:
(美)巴布·艾俊(Adrian Barbu)，朱松纯著
-
出版社:
电子工业出版社
-
ISBN:
9787121470202
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出版时间:
2024-01
售价
￥
88.60
6.4折
定价
￥138.00
品相
全新
上书时间2024-03-23
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作者简介
魏平，西安交通大学人工智能学院教授、博士生导师，国家青年人才，陕西高校青年创新团队（自主智能系统）带头人。西安交通大学学士、博士学位，美国加州大学洛杉矶分校（UCLA）博士后、联合培养博士。研究领域包括计算机视觉、机器学习、智能系统等，主持国家自然科学基金等等科研项目20余项，在TPAMI、CVPR、ICCV等有名期刊和会议发表学术论文多篇。担任中国自动化学会网联智能专委会副主任委员、中国图象图形学学会机器视觉专委会委员。获国家教学成果一等奖等荣誉奖励。
目录
目 录 第1 章 蒙特卡罗方法简介··············································································.1 1.1 引言·······························································································.1 1.2 动机和目标······················································································.1 1.3 蒙特卡罗计算中的任务·······································································.2 1.3.1 任务1：采样和模拟········································································.3 1.3.2 任务2：通过蒙特卡罗模拟估算未知量···················································.5 1.3.3 任务3：优化和贝叶斯推理································································.7 1.3.4 任务4：学习和模型估计···································································.8 1.3.5 任务5：可视化能级图·····································································.9 本章参考文献··························································································13 第2 章 序贯蒙特卡罗方法··············································································14 2.1 引言·······························································································14 2.2 一维密度采样···················································································14 2.3 重要性采样和加权样本·······································································15 2.4 序贯重要性采样（SIS） ······································································18 2.4.1 应用：表达聚合物生长的自避游走························································18 2.4.2 应用：目标跟踪的非线性/粒子滤波·······················································20 2.4.3 SMC 方法框架总结·········································································23 2.5 应用：利用SMC 方法进行光线追踪·······················································24 2.6 在重要性采样中保持样本多样性···························································25 2.6.1 基本方法····················································································25 2.6.2 Parzen 窗讨论··············································································28 2.7 蒙特卡罗树搜索················································································29 2.7.1 纯蒙特卡罗树搜索··········································································30 2.7.2 AlphaGo ·····················································································32 2.8 本章练习·························································································33 本章参考文献··························································································35 第3 章 马尔可夫链蒙特卡罗方法基础·······························································36 3.1 引言·······························································································36 蒙特卡罗方法与人工智能 ·X · 3.2 马尔可夫链基础················································································37 3.3 转移矩阵的拓扑：连通与周期······························································38 3.4 Perron-Frobenius 定理··········································································41 3.5 收敛性度量······················································································42 3.6 连续或异构状态空间中的马尔可夫链·····················································44 3.7 各态遍历性定理················································································45 3.8 通过模拟退火进行MCMC 优化·····························································46 3.9 本章练习·························································································49 本章参考文献··························································································51 第4 章 Metropolis 算法及其变体······································································52 4.1 引言·······························································································52 4.2 Metropolis-Hastings 算法······································································52 4.2.1 原始Metropolis-Hastings 算法······························································53 4.2.2 Metropolis-Hastings 算法的另一形式·······················································54 4.2.3 其他接受概率设计··········································································55 4.2.4 Metropolis 算法设计中的关键问题·························································55 4.3 独立Metropolis 采样···········································································55 4.3.1 IMS 的特征结构············································································56 4.3.2 有限空间的一般首中时·····································································57 4.3.3 IMS 击中时分析············································································57 4.4 可逆跳跃和跨维MCMC ······································································59 4.4.1 可逆跳跃····················································································59 4.4.2 简单例子：一维图像分割··································································60 4.5 应用：计算人数················································································63 4.5.1 标值点过程模型············································································64 4.5.2 MCMC 推理·················································································64 4.5.3 结果·························································································65 4.6 应用：家具布置················································································65 4.7 应用：场景合成················································································67 4.8 本章练习·························································································71 本章参考文献··························································································72 第5 章 吉布斯采样器及其变体········································································73 5.1 引言·······························································································73 5.2 吉布斯采样器···················································································74 目 录 ·XI· 5.2.1 吉布斯采样器介绍··········································································74 5.2.2 吉布斯采样器的一个主要问题·····························································75 5.3 吉布斯采样器扩展·············································································76 5.3.1 击中逃跑····················································································77 5.3.2 广义吉布斯采样器··········································································77 5.3.3 广义击中逃跑···············································································77 5.3.4 利用辅助变量采样··········································································78 5.3.5 模拟退火····················································································78 5.3.6 切片采样····················································································79 5.3.7 数据增强····················································································80 5.3.8 Metropolized 吉布斯采样器·································································80 5.4 数据关联和数据增强··········································································82 5.5 Julesz 系综和MCMC 纹理采样······························································83 5.5.1 Julesz 系综：纹理的数学定义······························································84 5.5.2 吉布斯系综和系综等价性··································································85 5.5.3 Julesz 系综采样·············································································86 5.5.4 实验：对Julesz 系综进行采样·····························································87 5.6 本章练习·························································································89 本章参考文献··························································································90 第6 章 聚类采样方法····················································································91 6.1 引言·······························································································91 6.2 Potts 模型和SW 算法·········································································92 6.3 SW 算法详解····················································································94 6.3.1 解释1：Metropolis-Hastings 观点··························································94 6.3.2 解释2：数据增强··········································································97 6.4 SW 算法的相关理论结果··································································.100 6.5 任意概率的SW 切分算法·································································.102 6.5.1 步骤一：数据驱动的聚类·······························································.102 6.5.2 步骤二：颜色翻转·······································································.103 6.5.3 步骤三：接受翻转·······································································.104 6.5.4 复杂性分析···············································································.105 6.6 聚类采样方法的变体·······································································.106 6.6.1 聚类吉布斯采样：“击中逃跑”观点·····················································.106 6.6.2 多重翻转方案············································································.107 6.7 应用：图像分割·············································································.107 蒙特卡罗方法与人工智能 ·X II· 6.8 多重网格和多级SW 切分算法···························································.110 6.8.1 多重网格SW 切分算法··································································.111 6.8.2 多级SW 切分算法·······································································.113 6.9 子空间聚类···················································································.114 6.9.1 通过SW 切分算法进行子空间聚类·····················································.115 6.9.2 应用：稀疏运动分割····································································.117 6.10 C 4：聚类合作竞争约束··································································.121 6.10
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