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Dimensional Analysis03:40

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Dimensional analysis, also known as the factor label method, is a versatile approach for mathematical operations. The main principle behind this approach is: the units of quantities must be subjected to the same mathematical operations as their associated numbers. This method can be applied to computations ranging from simple unit conversions to more complex and multi-step calculations involving several different quantities and their units.
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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
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一个基于有效确定标准的指标.

Jesús E García1, Verónica A González-López1, Johsac I Gomez Sanchez1

  • 1Department of Statistics, University of Campinas, Campinas 13083-859, São Paulo, Brazil.

Entropy (Basel, Switzerland)
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概括
此摘要是机器生成的。

本研究为分区马尔科夫模型 (PMM) 引入了新的指标,提高了模型选择的一致性. 有效的确定标准 (EDC) 提供了改进的参数惩罚,通过登革热病毒DNA序列进行验证.

关键词:
贝叶斯信息标准是贝叶斯信息标准.进入的过程中,分区 马尔科夫模型

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科学领域:

  • 计算生物学 计算生物学
  • 统计建模 统计建模
  • 生物信息学是一种生物信息学.

背景情况:

  • 分区马尔科夫模型 (PMM) 用于序列分析.
  • 贝叶斯信息标准 (BIC) 是PMM选择的常见指标.
  • 现有的BIC指标可能无法保证强烈一致的估计.

研究的目的:

  • 扩展基于BIC的指标,以实现强烈一致的PMM估计.
  • 引入和分析PMM选择的有效确定标准 (EDC).
  • 为了在EDC中确定改进的处罚功能.

主要方法:

  • 将BIC泛化为有效确定标准 (EDC) 家族.
  • 基于EDC指标的模型选择的正式规范.
  • 对EDC的罚款的识别和验证.
  • 在3型登革热病毒DNA序列建模中应用EDC指标.

主要成果:

  • EDC指标提供了比BIC更广泛的参数惩罚选项.
  • 在EDC内部的 ln(ln(n)) 处罚确保了非常一致的PMM估计.
  • 拟议的指标证明了分析现实世界生物数据的实用性.

结论:

  • EDC为PMM选择提供了一个强大的框架,并提供了更好的一致性保证.
  • 对于EDC来说,罚款在理论上是合理的,但在实践中也是有用的选择.
  • 这项工作推进了PMM分析,对基因组和流行病学研究有影响.