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摘要翻译：
研究了用不同阶次的参数模型学习产生的二元错误序列的总体。我们从纯随机伯努利序列中得到了它们的误差、算法复杂度和散度的估计。我们研究了这些变量与学习者信息密度参数之间的关系，该参数定义为包含学习者决策规则的压缩与未压缩文件长度之比。结果表明，好的学习者具有较低的信息密度$\Rho$，而差的学习者具有较高的$\Rho$。糟糕的学习者产生的错误序列不典型地复杂，或者与纯粹随机的伯努利序列随机偏离。好的学习者通常会产生比Bernoulli序列低发散度的复杂序列，它们包括由Bayes最优预测器产生的错误序列。基于静态算法干扰模型，学习器在这里充当一个静态结构，它将输入序列（待预测）的比特按其信息密度成比例地“分散”，从而使其随机性特征变形。
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英文标题：
《Random scattering of bits by prediction》
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作者：
Joel Ratsaby
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最新提交年份：
2010
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Computer Science 计算机科学
二级分类：Information Theory 信息论
分类描述：Covers theoretical and experimental aspects of information theory and coding. Includes material in ACM Subject Class E.4 and intersects with H.1.1.
涵盖信息论和编码的理论和实验方面。包括ACM学科类E.4中的材料，并与H.1.1有交集。
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一级分类：Mathematics 数学
二级分类：Information Theory 信息论
分类描述：math.IT is an alias for cs.IT. Covers theoretical and experimental aspects of information theory and coding.
它是cs.it的别名。涵盖信息论和编码的理论和实验方面。
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英文摘要：
We investigate a population of binary mistake sequences that result from learning with parametric models of different order. We obtain estimates of their error, algorithmic complexity and divergence from a purely random Bernoulli sequence. We study the relationship of these variables to the learner's information density parameter which is defined as the ratio between the lengths of the compressed to uncompressed files that contain the learner's decision rule. The results indicate that good learners have a low information density$\rho$ while bad learners have a high $\rho$. Bad learners generate mistake sequences that are atypically complex or diverge stochastically from a purely random Bernoulli sequence. Good learners generate typically complex sequences with low divergence from Bernoulli sequences and they include mistake sequences generated by the Bayes optimal predictor. Based on the static algorithmic interference model of \cite{Ratsaby_entropy} the learner here acts as a static structure which "scatters" the bits of an input sequence (to be predicted) in proportion to its information density $\rho$ thereby deforming its randomness characteristics.
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PDF链接：
https://arxiv.org/pdf/0909.3648
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