马科夫序列的透估计器:比较分析
Juan De Gregorio1, David Sánchez1, Raúl Toral1
1Institute for Cross-Disciplinary Physics and Complex Systems IFISC (UIB-CSIC), Campus Universitat de les Illes Balears, E-07122 Palma de Mallorca, Spain.
Entropy (Basel, Switzerland)
|January 22, 2024
概括
这项研究将序列的估计器与内存进行比较. 结果显示,性能因系统属性和数据大小而异,为信息理论应用提供了洞察力.
科学领域:
- 信息理论 信息理论
- 统计建模 统计建模
- 计算科学 计算科学
背景情况:
- 值估计在各种科学领域至关重要.
- 对具有内存的序列 (马科维系统) 进行准确的估计是具有挑战性的,因为数据限制和估计器偏差.
- 现有的估计器经常假设独立事件,限制它们对复杂系统的适用性.
研究的目的:
- 系统地比较各种估计器的性能,当应用到马科维数序列时.
- 分析系统属性的影响,如过渡概率和样本大小,对估计器的准确性.
- 为了确定局限性,并为存储系统中的估计提供指导.
主要方法:
- 使用二进制马可维数序列的常见估计器的评估.
- 对马科维系统的分析,特别是在样本不足的系统中.
- 对所选估计器进行偏差,标准偏差和平均平方误差的计算.
主要成果:
- 估计器的性能受到马尔科夫过程的过渡概率的显著影响.
- 在样本不足的制度中,估计器的准确性下降,突出显示了样本大小的影响.
- 不同的估计器表现出不同程度的偏差和变异,影响它们适用于特定的马科维亚系统的适用性.
结论:
- 没有一个单一的估计器是所有马科夫序列的普遍最佳.
- 了解估计器属性,系统内存和数据可用性之间的相互作用是准确值估计的关键.
- 这种比较分析为研究人员应用值估计到具有时间依赖性的系统提供了宝贵的见解.
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