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相关概念视频

Prediction Intervals01:03

Prediction Intervals

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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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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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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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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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Survival Tree01:19

Survival Tree

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
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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...
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相关实验视频

Updated: Jun 5, 2025

Trajectory Data Analyses for Pedestrian Space-time Activity Study
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在密集的数据中快速挖掘:应用深度第一顺序的概率支持预测.

Muhammad Sadeequllah1, Azhar Rauf1, Saif Ur Rehman1

  • 1Department of Computer Science, University of Peshawar, Peshawar, KP, Pakistan.

PeerJ. Computer science
|December 9, 2024
PubMed
概括
此摘要是机器生成的。

一个新的算法,Probabilistic Depth-First (ProbDF),通过概率地预测支持,有效地找到密集数据集中的频繁项目集,与宽度优先方法相比减少内存使用量.

关键词:
大约常见项目设置采矿.协会规则 采矿 采矿规则数据挖掘是一种数据挖掘.经常出现的项目 设置采矿交易数据库 交易数据库

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科学领域:

  • 数据挖掘和机器学习
  • 计算复杂性和算法设计

背景情况:

  • 频繁的项目集挖掘 (FIM) 对于关联规则挖掘至关重要,但计算密集,特别是对于密集的数据集.
  • 现有的近似FIM算法平衡了效率和准确性,但一些像Probabilistic Breadth-First (ProbBF) 这样的算法在密集的数据上遭受了高内存消耗.

研究的目的:

  • 提出一种新的FIM算法,即概率深度优先 (ProbDF),它解决了宽度优先方法的记忆效率低下问题.
  • 利用概率支持预测模型 (PSPM) 实现高效和可扩展的FIM,特别是对于密集的数据集.

主要方法:

  • ProbDF采用深度搜索策略,在最初的频繁项目集 (大小1和2) 被发现后,丢弃交易数据.
  • 它使用轻量级的概率支持预测模型 (PSPM) 来概率预测更大的项目集的支持,而无需访问交易数据.

主要成果:

  • 与现实世界基准数据集的现有方法相比,ProbDF在时间和空间上都表现出显著的效率.
  • 该算法成功识别了大多数频繁的项目集,展示了它对密集数据场景的有效性.

结论:

  • 对于频繁的项目集采矿,ProbDF提供了一个高效和内存意识的替代方案,特别是在密集的数据环境中.
  • 在实现高效率的同时,ProbDF的概率性质引入了具有绝对准确性的固有权衡.