resampling

简明释义

英[ˌriːˈsæmplɪŋ]美[ˌriːˈsæmplɪŋ]

n. [测]重采样；重新取样

v. 重新采样（resample 的 ing 形式）

英英释义

单词用法

同义词

反义词

例句

1.To evaluate software defect distribution exactly, a software defect prediction model based on AODE and resampling is put forward.

为了确切地估计软件缺陷分布，提出了基于AODE和再抽样的软件缺陷预测模型。

2.The design of the resampling filter is a key technique for symbol synchronization in all-digital receiver.

重采样滤波器设计是全数字接收机中实现符号同步的关键技术之一。

3.Local source data is first sampled at an original sampling rate and then resampled at a first resampling rate which is equal to the framing rate for transmitting said data to the remote source.

本地源数据首先以一个原始采样速率采样，然后以等于将所述数据发送到该远端源的帧速率的第一重复取样速率重复取样。

4.A digitized harmonic detection method based on resampling and feedback theory is proposed.

提出了基于重采样和反馈理论的数字化谐波检测方法。

5.Based on this transformation, standard spatial-compounding resampling tables can be used just as they are with curved arrays.

基于该转化，可以使用标准空间复合重采样表，正像其被用于曲面阵列那样。

6.In particle filters (PF), sequential importance sampling will result in sample impoverishment and further the loss of diversity after resampling.

粒子滤波算法(PF)中，序列重要性采样引起采样点贫化，进一步经过重采样后造成分集度损失。

7.Compared the three fusion methods and the three resampling techniques with themselves, the multiplicative fusion method and the cubic convolution resampling technique are relative better.

三种融合方法和三种重采样方式它们之问相比较而言，乘积法融合法和立方卷积重采样法相对较为可取。

8.The process of resampling is crucial in improving the accuracy of our statistical analysis.

在提高我们统计分析的准确性中，重采样过程至关重要。

9.We used resampling techniques to validate our model's predictions against the actual data.

我们使用重采样技术来验证模型预测与实际数据的一致性。

10.By resampling the dataset, we were able to reduce the variance in our results.

通过重采样数据集，我们能够减少结果中的方差。

11.In time series analysis, resampling can help to aggregate data into different time intervals.

在时间序列分析中，重采样可以帮助将数据聚合到不同的时间间隔。

12.The resampling method we chose significantly improved the robustness of our findings.

我们选择的重采样方法显著提高了我们发现的稳健性。

作文

In the field of statistics and data analysis, the concept of resampling is crucial for understanding how to make inferences about a population based on sample data. Resampling refers to the process of repeatedly drawing samples from a set of data and calculating a statistic for each sample. This technique allows researchers to assess the variability of their estimates and to create confidence intervals or hypothesis tests without relying solely on traditional parametric assumptions. One common method of resampling is the bootstrap method, which involves taking repeated samples from the original dataset, with replacement. Each of these samples can be used to calculate a statistic, such as the mean or median. By aggregating the results from these bootstrap samples, researchers can estimate the distribution of the statistic and derive confidence intervals. This approach is particularly useful when the underlying distribution of the data is unknown or when the sample size is small. Another popular resampling technique is cross-validation, which is primarily used in machine learning and predictive modeling. In this context, resampling involves partitioning the dataset into subsets, training a model on one subset, and validating it on another. This process is repeated multiple times with different partitions, allowing for a more robust evaluation of the model's performance. Cross-validation helps in preventing overfitting, ensuring that the model generalizes well to unseen data. The importance of resampling extends beyond just statistical analysis; it plays a significant role in various fields such as finance, biology, and environmental science. For instance, in finance, analysts may use resampling techniques to assess the risk of investment portfolios by simulating various market conditions. In biology, researchers might apply resampling to analyze genetic data, helping to identify significant associations between genes and diseases. Similarly, in environmental science, resampling methods can be employed to evaluate the impact of climate change on ecosystems by analyzing data collected over time. Despite its advantages, resampling is not without limitations. One challenge is that the results can be sensitive to the choice of the original sample. If the sample is not representative of the population, the resampling results may lead to misleading conclusions. Additionally, resampling can be computationally intensive, particularly with large datasets or complex models. Therefore, it is essential for researchers to be mindful of these factors when applying resampling techniques. In conclusion, resampling is a powerful statistical tool that enhances our ability to make informed decisions based on sample data. Whether through bootstrapping, cross-validation, or other methods, resampling provides valuable insights into the variability and reliability of statistical estimates. As we continue to generate vast amounts of data across various disciplines, mastering the art of resampling will become increasingly important for researchers and analysts alike.

在统计学和数据分析领域，重抽样的概念对于理解如何基于样本数据对总体进行推断至关重要。重抽样是指从一组数据中反复抽取样本并计算每个样本的统计量的过程。这种技术使研究人员能够评估其估计的变异性，并在不完全依赖传统参数假设的情况下创建置信区间或假设检验。 一种常见的重抽样方法是自助法（bootstrap），它涉及对原始数据集进行重复抽样，带有替换。每个这些样本都可以用来计算一个统计量，例如均值或中位数。通过聚合这些自助样本的结果，研究人员可以估计统计量的分布并推导出置信区间。这种方法在数据的基础分布未知或样本量较小时特别有用。 另一种流行的重抽样技术是交叉验证，这主要用于机器学习和预测建模。在这种情况下，重抽样涉及将数据集划分为子集，在一个子集上训练模型，并在另一个子集上验证它。这个过程在不同的划分下重复多次，从而更稳健地评估模型的性能。交叉验证有助于防止过拟合，确保模型能够很好地推广到未见过的数据。 重抽样的重要性不仅限于统计分析；它在金融、生物学和环境科学等多个领域发挥着重要作用。例如，在金融领域，分析师可能使用重抽样技术来评估投资组合的风险，通过模拟各种市场条件。在生物学中，研究人员可能应用重抽样来分析基因数据，帮助识别基因与疾病之间的重要关联。同样，在环境科学中，可以应用重抽样方法来评估气候变化对生态系统的影响，分析随时间收集的数据。 尽管有其优势，重抽样也并非没有局限性。一个挑战是结果可能对原始样本的选择敏感。如果样本不能代表总体，则重抽样结果可能导致误导性的结论。此外，重抽样在计算上可能非常密集，特别是在处理大型数据集或复杂模型时。因此，研究人员在应用重抽样技术时必须注意这些因素。 总之，重抽样是一种强大的统计工具，增强了我们基于样本数据做出明智决策的能力。无论是通过自助法、交叉验证还是其他方法，重抽样为统计估计的变异性和可靠性提供了宝贵的见解。随着我们在各个学科中继续生成大量数据，掌握重抽样的艺术对于研究人员和分析师来说将变得越来越重要。