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随机链长度可变存储器和算法上下文
Stochastic chains with variable length memory and the algorithm Context |
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| 课程网址: |
http://videolectures.net/acs07_galves_sc/
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| 主讲教师: |
Antonio Galves
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| 开课单位: |
圣保罗大学
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| 开课时间: |
2007-12-10
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| 课程语种: |
英语
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| 中文简介: |
具有可变长度记忆的随机链定义了有限字母表上无限阶随机链的一个有趣的家族。其想法是, 对于每一个过去, 只有过去的有限后缀, 称为 "上下文", 就足以预测下一个符号。上下文集可以由具有有限标记分支的根树表示。链的定律的特点是它的上下文树和由树索引的过渡概率的关联家族。这些模型最初是由 Rissanen (1983年) 在信息论文献中引入的, 作为执行数据压缩的通用工具。最近, 它们被用来模拟生物学、语言学和音乐等不同领域的科学数据。这些模型最初被称为 "有限的内存源" 或 "的树机", 这些模型在统计文献中变得相当流行, 以 "可变长度马尔可夫链的名义" 由 buhlmann 和 wyner 创造的 (1999年)。在我的演讲中, 我将介绍一些在这一领域应用的基本想法、问题和例子。我将重点介绍算法上下文, 它估计上下文树和定义链的过渡概率的关联族。
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| 课程简介: |
Stochastic chains with variable length memory define an interesting family of stochastic chains of infinite order on a finite alphabet. The idea is that for each past, only a finite suffix of the past, called "context", is enough to predict the next symbol. The set of contexts can be represented by a rooted tree with finite labeled branches. The law of the chain is characterized by its tree of contexts and by an associated family of transition probabilities indexed by the tree. These models were first introduced in the information theory literature by Rissanen (1983) as an universal tool to perform data compression. Recently, they have been used to model up scientific data in areas as different as biology, linguistics and music. Originally called "finite memory source" or "tree machines", these models became quite popular in the statistics literature under the name of "Variable Length Markov Chains" coined by Buhlmann and Wyner (1999). In my talk I will present some of the basic ideas, problems and examples of application in the field. I will focus on the algorithm Context which estimates the tree of contexts and the associated family of transition probabilities defining the chain.
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| 关 键 词: |
随机链; 储器; 算法
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| 课程来源: |
视频讲座网
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| 最后编审: |
2020-06-13：邬启凡(课程编辑志愿者)
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| 阅读次数: |
37
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