poisson

简明释义

英[pwɑːsɒn]美[pwɑːˈsɑn]

n. 泊松；（Poisson）（法）普瓦松（人名）

英英释义

单词用法

同义词

反义词

例句

1.Based on the Bayesian theory, a probability method was proposed to estimate the parameters of the Poisson curve.

基于贝叶斯理论，提出采用概率的方法来估计泊松曲线的参数。

2.By studying the simplest flow (Poisson process), the new statistical method is set up and it is called the simplest fault flow method.

这种可靠性分析方法是通过对最简单流(泊松过程)的研究建立起来的，故称之为最简单故障流方法。

3.Poisson laid the theoretical foundation of seismic wave.

奠定了地震波理论基础。

4.This paper develops a general analytical poisson queue model, which can efficiently depict distributed architecture of the high-end router.

提出了一种基于泊松分布的排队模型，该模型能够有效地描述分布式路由器体系结构。

5.This paper develops a general analytical poisson queue model, which can efficiently depict distributed architecture of the high-end router.

提出了一种基于泊松分布的排队模型，该模型能够有效地描述分布式路由器体系结构。

6.The Schrdinger equation and Poisson equation are solved self-consistently to calculate the new two dimensional surface states.

从薛定谔方程和泊松方程的自洽计算中得到了新的二维表面态。

7.The dynamical Poisson ratio, dynamical Young's modulus and dynamical compressive yield strength were obtained.

得到了水泥砂浆石的动态泊松比、动态杨氏模量、动态压缩强度。

8.The impulse consumption control strategy of the problem is governed by a mixed process-geometrical Brownian motion and a Poisson process.

讨论了一类随机控制问题，其脉冲消费控制策略受控于一混合过程——几何布朗运动和泊松过程。

9.I ordered a delicious poisson for dinner.

我点了一道美味的鱼作为晚餐。

10.I love to eat poisson grilled with lemon.

我喜欢吃用柠檬烤制的鱼。

11.In the market, you can find fresh poisson every day.

在市场上，你每天都能找到新鲜的鱼。

12.The chef prepared a special poisson dish with herbs.

厨师用香草准备了一道特别的鱼菜。

13.The restaurant is famous for its fried poisson.

这家餐厅以其炸鱼而闻名。

作文

In the realm of statistics and probability, the term poisson refers to a specific type of distribution that is used to model the number of events occurring within a fixed interval of time or space. This distribution is particularly useful in scenarios where these events occur independently of one another. For instance, consider a call center that receives an average of five calls per hour. The number of calls received in any given hour can be modeled using a poisson distribution. By using this statistical tool, managers can predict the likelihood of receiving a certain number of calls during a busy period, helping them allocate resources more effectively.The poisson distribution is defined by a single parameter, usually denoted by λ (lambda), which represents the average rate of occurrence for the event being measured. In our call center example, λ would be five, indicating that on average, five calls are received per hour. The probability of receiving exactly k calls in an hour can be calculated using the formula:P(X = k) = (e^(-λ) * λ^k) / k!,where e is the base of the natural logarithm, and k! is the factorial of k. This formula allows us to calculate the likelihood of various outcomes, such as receiving no calls at all or receiving ten calls in an hour.One of the key characteristics of the poisson distribution is that it is discrete, meaning it only takes on non-negative integer values. This makes it particularly suitable for counting events, such as the number of emails received in a day or the number of accidents occurring at a traffic intersection over a month. It is important to note that while the poisson distribution can model rare events, it is most accurate when the average number of occurrences is relatively low.In addition to its applications in business and telecommunications, the poisson distribution also finds relevance in various fields, including healthcare, environmental science, and engineering. For example, researchers might use it to model the number of patients arriving at a hospital emergency room within a given time frame or to estimate the frequency of earthquakes in a particular region.Understanding the principles behind the poisson distribution can greatly enhance decision-making processes in diverse areas. By applying this statistical method, organizations can gain insights into patterns and trends, enabling them to make informed predictions about future occurrences. This predictive capability is invaluable in resource planning and risk management, as it allows businesses to prepare for fluctuations in demand or unexpected events.In conclusion, the term poisson is not just a statistical concept; it is a powerful tool that provides valuable insights across various domains. Whether in the context of managing a busy call center or analyzing patient flow in hospitals, the poisson distribution offers a structured approach to understanding and predicting the frequency of events. As we continue to navigate an increasingly data-driven world, mastering concepts like the poisson distribution will undoubtedly prove essential for professionals in numerous fields.

在统计学和概率论的领域中，术语poisson指的是一种特定类型的分布，用于建模在固定时间或空间间隔内发生的事件数量。这种分布在事件彼此独立发生的情况下特别有用。例如，考虑一个每小时平均接到五个电话的呼叫中心。在任何给定的小时内接到的电话数量可以使用poisson分布进行建模。通过使用这一统计工具，管理人员可以预测在繁忙时段接到一定数量电话的可能性，从而帮助他们更有效地分配资源。poisson分布由一个单一参数定义，通常用λ（lambda）表示，代表所测量事件的平均发生率。在我们的呼叫中心示例中，λ将是五，表示平均每小时接到五个电话。在一小时内接到恰好k个电话的概率可以使用以下公式计算：P(X = k) = (e^(-λ) * λ^k) / k!，其中e是自然对数的底数，k!是k的阶乘。这个公式使我们能够计算各种结果的可能性，例如完全没有电话或在一个小时内接到十个电话。poisson分布的一个关键特征是它是离散的，意味着它只能取非负整数值。这使得它特别适合于计数事件，例如每天接收到的电子邮件数量或一个月内发生在交通路口的事故数量。值得注意的是，虽然poisson分布可以建模稀有事件，但当平均发生次数相对较低时，它最为准确。除了在商业和电信领域的应用外，poisson分布还在医疗保健、环境科学和工程等多个领域具有相关性。例如，研究人员可能会使用它来建模在特定时间框架内到达医院急诊室的患者数量，或估算某一地区地震发生的频率。理解poisson分布背后的原则可以大大增强各个领域的决策过程。通过应用这一统计方法，组织可以获得关于模式和趋势的洞察，使他们能够对未来的发生情况做出明智的预测。这种预测能力在资源规划和风险管理中是无价的，因为它使企业能够为需求波动或意外事件做好准备。总之，术语poisson不仅仅是一个统计概念；它是一个强大的工具，在各个领域提供了宝贵的见解。无论是在管理繁忙的呼叫中心还是分析医院的患者流量方面，poisson分布都提供了一种结构化的方法来理解和预测事件的频率。随着我们继续在一个日益数据驱动的世界中航行，掌握像poisson分布这样的概念无疑将对众多领域的专业人士至关重要。