multivariable

简明释义

英[ˌmʌltiˈvɛrɪbəl]美[ˌmʌltiˈvɛrɪbəl]

adj. 多变量的（等于 multivariate）；多元的

英英释义

单词用法

同义词

反义词

例句

1.Multivariable equation set fitting method is derived on the basis of least square method principle.

根据最小二乘法的求解原理，建立了一种适用于多变量方程组系数参数的拟合算法。

2.Multivariable equation set fitting method is derived on the basis of least square method principle.

根据最小二乘法的求解原理，建立了一种适用于多变量方程组系数参数的拟合算法。

3.The transportation energy demand system is a complex, multivariable, and nonlinear system.

交通能源需求系统是一个复杂多变量的非线性系统。

4.The truck dispatching can be taken as a multivariable optimization problem.

卡车调度可以化为一个受多变量影响的最值问题。

5.Decoupling control is an effective method in multivariable system control.

解耦控制是多变量系统控制的有效手段。

6.Two-phase flow is a complex multivariable random process.

两相流的流动是一个复杂的多变量随机过程。

7.You should have a good book on multivariable calculus.

你们应该有一本关于多变量微积分的好书。

8.And here, it's the same thing in multivariable calculus.

这里，在多元微积分里面也一样。

9.In statistics, a multivariable analysis is essential for understanding complex relationships between multiple factors.

在统计学中，多变量分析对于理解多个因素之间的复杂关系至关重要。

10.The research team used a multivariable model to predict the outcomes of the experiment based on several input variables.

研究小组使用了一个多变量模型，根据几个输入变量预测实验结果。

11.When designing a product, engineers often consider multivariable factors such as cost, durability, and user experience.

在设计产品时，工程师通常会考虑多变量因素，如成本、耐用性和用户体验。

12.In machine learning, multivariable regression can help in forecasting trends using several independent variables.

在机器学习中，多变量回归可以帮助使用多个自变量来预测趋势。

13.The patient’s health was affected by multivariable conditions, including diet, exercise, and genetics.

患者的健康受到多变量因素的影响，包括饮食、锻炼和遗传。

作文

In the realm of mathematics and statistics, the concept of multivariable functions plays a crucial role in understanding complex systems. A multivariable function is one that depends on two or more variables. This is in contrast to univariate functions, which depend solely on a single variable. The study of multivariable functions allows us to analyze how changes in multiple factors can affect outcomes in various fields such as economics, physics, biology, and engineering. For instance, consider a scenario in economics where the price of a product is influenced by several factors: supply, demand, production costs, and consumer preferences. In this case, we can represent the relationship using a multivariable function, where the price is a function of these multiple variables. By employing calculus, we can find the partial derivatives of this multivariable function to determine how sensitive the price is to changes in each of these factors. This analysis is essential for businesses aiming to optimize their pricing strategies. In physics, the motion of an object can also be described using multivariable functions. For example, the position of a particle moving in three-dimensional space can be expressed as a function of time and its coordinates (x, y, z). Here, the position is a multivariable function of time, as it depends on multiple variables simultaneously. Understanding this relationship helps physicists predict future positions and understand the dynamics of the system. Moreover, in the field of biology, multivariable models are often used to study population dynamics. Factors such as birth rates, death rates, immigration, and emigration can all influence the size of a population. By creating a multivariable model that incorporates these variables, biologists can simulate different scenarios and predict how populations will change over time. This information is vital for conservation efforts and managing ecosystems effectively. The importance of multivariable analysis extends beyond theoretical applications; it is also fundamental in data science and machine learning. Many algorithms rely on multivariable functions to make predictions based on multiple input features. For example, a machine learning model predicting house prices may take into account various factors such as location, size, number of bedrooms, and age of the property. Each of these factors represents a variable in a multivariable function that collectively influences the predicted price. Understanding how to work with multivariable data is essential for data scientists aiming to build accurate and robust models. In conclusion, the concept of multivariable functions is integral to many disciplines, enabling us to understand and analyze the interplay between multiple factors. Whether in economics, physics, biology, or data science, the ability to model relationships involving several variables enhances our capacity to make informed decisions and predictions. As we continue to face increasingly complex challenges in the modern world, mastering the principles of multivariable analysis will be invaluable in developing effective solutions.

在数学和统计学领域，多变量函数的概念在理解复杂系统中发挥着至关重要的作用。多变量函数是指依赖于两个或多个变量的函数。这与仅依赖单一变量的单变量函数相对。研究多变量函数使我们能够分析多个因素如何影响经济学、物理学、生物学和工程等各个领域的结果。 例如，考虑一个经济学场景，其中产品的价格受多个因素的影响：供给、需求、生产成本和消费者偏好。在这种情况下，我们可以使用多变量函数来表示这种关系，其中价格是这些多个变量的函数。通过使用微积分，我们可以找到这个多变量函数的偏导数，以确定价格对每个因素变化的敏感性。这种分析对于希望优化定价策略的企业至关重要。 在物理学中，物体的运动也可以用多变量函数来描述。例如，描述在三维空间中移动的粒子的位置可以表示为时间及其坐标（x，y，z）的函数。在这里，位置是时间的多变量函数，因为它同时依赖于多个变量。理解这种关系有助于物理学家预测未来的位置，并理解系统的动态。 此外，在生物学领域，多变量模型常用于研究种群动态。出生率、死亡率、移民和移民等因素都会影响种群的大小。通过创建一个包含这些变量的多变量模型，生物学家可以模拟不同的情景并预测种群如何随时间变化。这些信息对于保护工作和有效管理生态系统至关重要。 多变量分析的重要性不仅限于理论应用；它在数据科学和机器学习中也是基础。许多算法依赖于多变量函数根据多个输入特征进行预测。例如，一个预测房价的机器学习模型可能会考虑多个因素，如位置、大小、卧室数量和房产年龄。每个因素都代表了一个多变量函数中的变量，这些变量共同影响预测的价格。理解如何处理多变量数据对于希望构建准确且稳健模型的数据科学家至关重要。 总之，多变量函数的概念是许多学科的核心，使我们能够理解和分析多个因素之间的相互作用。无论是在经济学、物理学、生物学还是数据科学中，建模涉及多个变量的关系的能力增强了我们做出明智决策和预测的能力。随着我们继续面临现代世界日益复杂的挑战，掌握多变量分析的原则对于开发有效解决方案将是无价的。