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相关概念视频

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

54
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.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
54
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

684
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
684
Convergence of Fourier Series01:21

Convergence of Fourier Series

148
The Fourier series is a powerful mathematical tool for representing periodic signals as an infinite sum of complex exponentials. In practice, this infinite series is truncated to a finite number of terms, yielding a partial sum. This truncation makes the approximation of the signal feasible but introduces certain challenges, particularly near discontinuities, known as the Gibbs phenomenon.
The Gibbs phenomenon refers to the persistent oscillations and overshoots that occur near discontinuities...
148
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

81
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
81
Upsampling01:22

Upsampling

236
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
236
Area Computation by the Alternative Coordinate Method01:24

Area Computation by the Alternative Coordinate Method

53
The alternative coordinate method, also known as the Shoelace Formula, is a technique for determining the area of a traverse using Cartesian coordinates. This method relies on the sequential arrangement of x and y coordinates for each point of the shape, ensuring accuracy and ease of application.In this approach, each corner's x and y coordinates are listed as fractions, with the x-coordinate as the numerator and the y-coordinate as the denominator. These coordinates are arranged sequentially...
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一个墙时间最小化的平行化策略,用于近似贝叶斯计算.

Emad Alamoudi1, Felipe Reck1, Nils Bundgaard2

  • 1Life and Medical Sciences Institute, University of Bonn, Bonn, Germany.

PloS one
|February 22, 2024
PubMed
概括
此摘要是机器生成的。

展望未来的调度优化了近似贝叶斯计算 (ABC) 序列蒙特卡洛算法,通过预先采样,最大限度地减少计算时间. 这一策略充分利用计算资源,提高了机械模型参数估计的效率.

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科学领域:

  • 计算统计学 计算统计学
  • 统计建模 统计建模
  • 高性能计算 高性能计算

背景情况:

  • 大致贝叶斯计算 (ABC) 是复杂机械模型中参数估计的流行的方法.
  • ABC分析是计算密集型的,需要对高性能计算 (HPC) 基础设施进行并行处理.
  • 目前ABC的并行化策略往往导致未充分利用的计算资源和低于最佳的效率.

研究的目的:

  • 引入和评估一种新的并行化策略,即"前性调度",用于ABC 序列蒙特卡洛 (SMC) 算法.
  • 为了最大限度地减少墙壁时钟的时间和最大限度地利用资源,在计算上昂贵的ABC分析中.
  • 为拟议的先发性抽样方法提供理论理由和经验验证.

主要方法:

  • 开发一个展望未来的调度策略,包括在ABC-SMC中先发性抽样下一代.
  • 预先抽样策略的理论评估和不偏见证明.
  • 在各种问题和不同数量的并行核心中实施和经验评估战略.

主要成果:

  • 展望未来的调度策略通过利用所有可用的资源,有效地减少了计算单元的置时间.
  • 理论评估证实了先发性抽样方法的公正性.
  • 经验评估表明,与既定的并行化方法相比,显著加快速度,通常为10-20%,高达50%.

结论:

  • 展望日程安排在HPC基础设施上的ABC方法的成本和运行时间效率得到了大幅度的改善.
  • 该策略与实用增强相兼容,例如自适应距离函数和总结统计选择.
  • 这种优化的并行化方法使得使用ABC的机械模型可以更有效地估计参数.