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相关概念视频

Entropy Change in Reversible Processes01:10

Entropy Change in Reversible Processes

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In the Carnot engine, which achieves the maximum efficiency between two reservoirs of fixed temperatures, the total change in entropy is zero. The observation can be generalized by considering any reversible cyclic process consisting of many Carnot cycles. Thus, it can be stated that the total entropy change of any ideal reversible cycle is zero.
The statement can be further generalized to prove that entropy is a state function. Take a cyclic process between any two points on a p-V diagram.
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Probability in Statistics01:14

Probability in Statistics

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Probability is the likelihood of an event occurring. The term event is defined as a collection of results of a procedure. An event is a simple event when an outcome cannot be divided into simpler parts.
An example of a simple event is a coin toss. The result of a coin toss is either a head or a tail. Here, head and tail are two simple events. These two simple events make up the sample space. Further, the probability of an event occurring falls within the range of 0 to 1. The probability of an...
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Probability Histograms01:17

Probability Histograms

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A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
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Random Variables01:09

Random Variables

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A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
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Poisson Probability Distribution01:09

Poisson Probability Distribution

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A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
The...
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Probability Distributions01:32

Probability Distributions

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 The probability of a random variable x  is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
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Non-invasive Assessments of Subjective and Objective Recovery Characteristics Following an Exhaustive Jump Protocol
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Non-invasive Assessments of Subjective and Objective Recovery Characteristics Following an Exhaustive Jump Protocol

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跳跃过程的极值统计数据.

J Klinger1,2, R Voituriez1,2, O Bénichou1

  • 1Laboratoire de Physique Théorique de la Matière Condensée, CNRS/Sorbonne Université, 4 Place Jussieu, 75005 Paris, France.

Physical review. E
|June 22, 2024
PubMed
概括

本研究探讨了跳跃过程的极端值统计. 我们发现关键概率,如半无限传播器和条形概率,对于理解过程极端及其时机至关重要.

科学领域:

  • 可能性理论概率理论.
  • 随机过程 随机过程
  • 统计物理 统计物理

背景情况:

  • 极端值统计 (EVS) 对于理解复杂系统中的罕见事件至关重要.
  • 跳跃过程,以不连续的运动为特征,是各种科学领域的基本模型.
  • 分析这些过程的极端需要专门的概率工具.

研究的目的:

  • 对一般离散时间和连续空间对称跳跃过程进行极端值统计 (EVS) 的研究.
  • 确定控制这些过程EVS的关键概率量.
  • 为了获得极端的联合分布及其相关时间的确切表达式和普遍的非对称行为.

主要方法:

  • 对于无边界跳跃过程,使用了半无限传播器G_{0}(x,n).
  • 对于有界的,半无限的跳跃过程,引入并分析了条形概率μ_{0,[下 x]̲}(n).
  • 数学导数被用来获得相关分布的确切表达式和非对称行为.

主要成果:

  • 半无限传播器G_{0}(x,n) 已被证明对于在无边界跳跃过程中导出极端的联合分布及其撞击时间至关重要.
  • 线条概率 μ_{0,[under x]̲}(n) 被确定为在有限的,半无限的跳跃过程中分析EVS的基本量.
  • 对于与过程极端相关的各种联合分布的普遍非对称行为,对于这两种类型的过程都被提取了.

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相关实验视频

Last Updated: Jun 23, 2025

Non-invasive Assessments of Subjective and Objective Recovery Characteristics Following an Exhaustive Jump Protocol
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Non-invasive Assessments of Subjective and Objective Recovery Characteristics Following an Exhaustive Jump Protocol

Published on: June 8, 2017

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Observation and Analysis of Blinking Surface-enhanced Raman Scattering
05:52

Observation and Analysis of Blinking Surface-enhanced Raman Scattering

Published on: January 11, 2018

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Quantifying Spatiotemporal Parameters of Cellular Exocytosis in Micropatterned Cells

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结论:

  • 该研究提供了一个统一的框架,用于分析极端值统计数据在一个广泛的类型的对称跳跃过程.
  • 确定了关键的概率量为未来极端事件研究提供了强大的工具.
  • 这些发现有助于更深入地了解在极端的随机系统的行为.