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相关概念视频

Truncation in Survival Analysis01:09

Truncation in Survival Analysis

241
Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
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Binomial Probability Distribution01:15

Binomial Probability Distribution

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A binomial distribution is a probability distribution for a procedure with a fixed number of trials, where each trial can have only two outcomes.
The outcomes of a binomial experiment fit a binomial probability distribution. A statistical experiment can be classified as a binomial experiment if the following conditions are met:
There are a fixed number of trials. Think of trials as repetitions of an experiment. The letter n denotes the number of trials.
There are only two possible outcomes,...
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Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
732
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...
12.4K
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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相关实验视频

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A Tactile Automated Passive-Finger Stimulator TAPS
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贝叶斯函数注册与随机截断的贝叶斯函数注册

Yi Lu1, Radu Herbei2, Sebastian Kurtek2

  • 1Mathematics and Computer Science Department, Drew University, Madison, New Jersey, United States of America.

PloS one
|July 7, 2023
PubMed
概括
此摘要是机器生成的。

本研究引入了新的贝叶斯函数注册模型,使用随机维度减小技术. 这种方法提高了时间扭曲函数的灵活性和数据驱动的流性推断.

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相关实验视频

Last Updated: Jul 24, 2025

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科学领域:

  • 统计 统计 统计 统计
  • 计算数学 计算数学 计算数学
  • 功能数据分析 功能数据分析

背景情况:

  • 在各种科学领域中,实值函数的注册至关重要.
  • 现有的贝叶斯模型经常使用固定维度减小规则,限制了适应性.
  • 无限维的函数空间给实际分析带来了计算上的挑战.

研究的目的:

  • 为实值函数注册开发新的贝叶斯模型.
  • 在功能模型中引入一个随机截断规则来进行尺寸缩小.
  • 为了能够基于数据推断功能参数的光滑性和形状的改变.

主要方法:

  • 贝叶斯模型与高斯过程 priors 在时间扭曲函数上.
  • 马尔科夫链蒙特卡洛 (MCMC) 的应用,用于后期分布勘探.
  • 随机缩小尺寸与固定截断方法形成鲜明对比.

主要成果:

  • 新的模型允许推断功能参数的平滑性.
  • 截断规则是数据信息的,适应观察到的函数中的局部特征.
  • 通过使用模拟和真实数据在注册过程中控制形状变化的灵活性.

结论:

  • 随机化维度减小在贝叶斯函数注册中比固定的规则具有优势.
  • 拟议的模型为分析功能数据提供了更适应和信息化的方法.
  • 该方法允许基于数据复杂性的后部分布的自动集中.