admissible parameter

简明释义

容许参数;

英英释义

例句

1.In statistical modeling, the choice of an admissible parameter 可接受参数 is crucial for ensuring accurate predictions.

在统计建模中，选择一个可接受参数是确保准确预测的关键。

2.When optimizing a function, it is important to define the admissible parameter 可接受参数 space correctly.

在优化一个函数时，正确地定义可接受参数空间是很重要的。

3.In machine learning, an admissible parameter 可接受参数 can prevent overfitting during training.

在机器学习中，一个可接受参数可以防止训练过程中的过拟合。

4.The model's performance improved significantly once we adjusted the admissible parameter 可接受参数 settings.

一旦我们调整了可接受参数设置，模型的性能显著提高。

5.The researchers identified the admissible parameter 可接受参数 range for their experiment to maintain validity.

研究人员确定了他们实验的可接受参数范围，以保持有效性。

作文

In the realm of mathematics and statistics, the concept of an admissible parameter is crucial for understanding the behavior of various models and systems. An admissible parameter refers to a value or set of values that are acceptable within a specific framework or model, allowing researchers and analysts to draw meaningful conclusions from their data. This term often arises in discussions about optimization problems, statistical estimation, and decision theory, where the choice of parameters can significantly impact the results and interpretations of a study.To illustrate the importance of admissible parameters, consider a scenario in which a researcher is developing a predictive model for housing prices. The researcher must select several parameters that influence the price, such as location, square footage, number of bedrooms, and so forth. However, not all combinations of these parameters will yield valid or useful predictions. For instance, if the model includes a parameter that exceeds the physical limits of housing (like a negative square footage), it would not be considered an admissible parameter. Therefore, the selection of admissible parameters is essential to ensure the integrity and applicability of the model.Moreover, in statistical theory, the concept of admissible parameters becomes even more significant when discussing estimators. An estimator is said to be admissible if there is no other estimator that performs better in terms of risk or error for all possible parameter values. In this context, identifying admissible parameters helps statisticians determine the most efficient methods for estimating population parameters based on sample data. By focusing on admissible parameters, researchers can enhance the reliability of their findings and contribute valuable insights to their fields.In decision-making processes, particularly in economics and finance, the notion of admissible parameters plays a vital role as well. When evaluating investment opportunities, analysts must consider various parameters such as risk, return, and market conditions. Only those parameters that are deemed admissible parameters can be used to construct a viable investment strategy. If an analyst were to include parameters that are unrealistic or not applicable to the current market scenario, the resulting strategy could lead to significant losses or missed opportunities.Furthermore, the identification of admissible parameters is not a one-time task; it requires continuous evaluation and adjustment as new data and insights emerge. For instance, in the field of machine learning, models are regularly updated with fresh data, which may lead to a re-evaluation of what constitutes admissible parameters. As algorithms learn from new information, they must adapt to include only those parameters that remain relevant and valid. This dynamic nature of parameter selection ensures that models stay accurate and effective over time.In conclusion, the concept of admissible parameters is fundamental across various disciplines, including mathematics, statistics, economics, and machine learning. Understanding what constitutes an admissible parameter allows researchers and practitioners to build robust models, make informed decisions, and derive meaningful insights from their analyses. As the landscape of data continues to evolve, the significance of identifying and utilizing admissible parameters will only grow, reinforcing their importance in both theoretical research and practical applications.

在数学和统计学领域，可接受参数的概念对于理解各种模型和系统的行为至关重要。可接受参数是指在特定框架或模型内可接受的值或值集，使研究人员和分析师能够从数据中得出有意义的结论。这个术语通常出现在关于优化问题、统计估计和决策理论的讨论中，在这些情况下，参数的选择会显著影响研究的结果和解释。为了说明可接受参数的重要性，考虑一个研究者正在开发房价预测模型的场景。研究者必须选择几个影响价格的参数，比如位置、面积、卧室数量等等。然而，并不是所有这些参数的组合都会产生有效或有用的预测。例如，如果模型包含一个超出房屋物理限制的参数（比如负面积），那么它就不会被视为可接受参数。因此，选择可接受参数对于确保模型的完整性和适用性至关重要。此外，在统计理论中，当讨论估计量时，可接受参数的概念变得更加重要。一个估计量如果在所有可能的参数值下没有其他估计量在风险或误差方面表现更好，就被称为可接受的。在这种情况下，识别可接受参数帮助统计学家确定基于样本数据估计总体参数的最有效方法。通过关注可接受参数，研究人员可以提高其研究发现的可靠性，并为其领域贡献有价值的见解。在经济学和金融学中的决策过程中，可接受参数的概念同样发挥着重要作用。当评估投资机会时，分析师必须考虑各种参数，如风险、收益和市场状况。只有那些被认为是可接受参数的参数才能用于构建可行的投资策略。如果一位分析师包含了不现实或与当前市场情境不相符的参数，所产生的策略可能会导致重大损失或错失机会。此外，识别可接受参数并不是一次性的任务；它需要随着新数据和见解的出现不断评估和调整。例如，在机器学习领域，模型会定期用新数据更新，这可能导致对什么构成可接受参数的重新评估。随着算法从新信息中学习，它们必须适应，只包括那些仍然相关和有效的参数。这种参数选择的动态特性确保了模型随着时间的推移保持准确和有效。总之，可接受参数的概念在数学、统计学、经济学和机器学习等多个学科中都是基础性的。理解什么构成可接受参数使研究人员和从业者能够构建稳健的模型、做出明智的决策，并从分析中得出有意义的见解。随着数据环境的不断演变，识别和利用可接受参数的重要性只会增加，进一步强化了它们在理论研究和实际应用中的重要性。