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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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Time-Series Graph00:54

Time-Series Graph

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A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
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Per-Unit Sequence Models01:26

Per-Unit Sequence Models

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An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
87
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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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
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Associative Learning01:27

Associative Learning

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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
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相关实验视频

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Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
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通过深度条件生成学习对时间序列中的马尔科夫属性进行测试.

Yunzhe Zhou1, Chengchun Shi2, Lexin Li1

  • 1Division of Biostatistics, University of California at Berkeley, Berkeley, CA, USA.

Journal of the Royal Statistical Society. Series B, Statistical methodology
|October 2, 2023
PubMed
概括

我们使用深度学习开发了一种新的非参数测试,用于高维时间序列中的马尔科夫属性. 这种方法准确地识别了马尔科夫属性,并确定了马尔科夫模型的顺序.

科学领域:

  • 统计 统计 统计 统计
  • 机器学习 机器学习
  • 时间序列分析时间序列分析

背景情况:

  • 马尔科夫属性是时间序列分析的基础,对于模拟顺序数据至关重要.
  • 测试这个属性和确定马尔科夫模型的顺序是统计推理中的重要任务.

研究的目的:

  • 为高维时间序列中马尔科夫属性的新型非参数测试提出建议.
  • 为了扩展这个测试,对马尔科夫模型顺序的顺序确定.
  • 为了建立测试性能的理论保证.

主要方法:

  • 使用深度条件生成学习来估计条件密度函数.
  • 开发一个双重可靠的测试统计数据,使用非参数估计和参数收率.
  • 采用样品分割和交叉拟合,以提高测试一致性.

主要成果:

  • 拟议的测试以非对称的方式控制了I型错误率.
  • 测试表明功率接近一个,表明高检测能力.
  • 理论分析为估计错误提供了明确的上限.

结论:

  • 基于深度学习的非参数测试对高维时间序列有效.
关键词:
马尔科夫的财产是马尔科夫的财产.深度有条件的生成性学习.高维的时间序列.假设测试 测试 假设测试混合物密度网络 混合物密度网络

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  • 该方法为马尔科夫属性测试和模型订单选择提供了一个强大的方法.
  • 通过模拟和现实世界的数据应用来证明有效性.