普森抽样 随机盗与杂的背景:一个信息理论遗憾分析
Sharu Theresa Jose1, Shana Moothedath2
1School of Computer Science, University of Birmingham, Birmingham B15 2TT, UK.
Entropy (Basel, Switzerland)
|July 26, 2024
概括
本研究引入了针对杂的上下文盗 (CB) 的修改的普森采样算法. 该算法接近一个预言策略,实现近最佳的贝叶斯累积遗憾缩放为高斯强盗的O ̃(mT).
科学领域:
- 机器学习 机器学习
- 强化学习是一种强化学习.
- 信息理论 信息理论
背景情况:
- 语境带 (CB) 对于具有部分反的顺序决策至关重要.
- 现实世界的CB通常涉及杂的上下文观察,使政策设计复杂化.
- 现有的方法在CB中与未知的噪声通道参数作斗争.
研究的目的:
- 设计一个有效的行动政策,用于随机线性上下文盗与杂的上下文观测.
- 为了近似贝叶斯预言的性能,可以访问真正的上下文和奖励模型.
- 用信息理论工具分析贝叶斯对拟议政策的累积遗憾.
主要方法:
- 引入了一个改进的普森采样算法,适用于噪音较大的CB.
- 采用信息理论分析来得出遗憾的界限.
- 研究了延迟上下文信息对遗憾的影响.
- 根据已建立的基线算法进行经验评估.
主要成果:
- 拟议的算法实现贝叶斯累积遗憾缩放为O ̃(mT) 对于在特定先前方差条件下具有高斯语境噪声的高斯语境盗.
- 证明推迟真实上下文观察可以导致遗憾减少.
- 经验结果验证了算法的性能与基线相比.
结论:
- 修改后的普森采样算法为有噪音的环境中的情境盗提供了强大的解决方案.
- 这些发现提供了理论上的保证和实际的见解,用于管理CB的上下文不确定性.
- 延迟的上下文信息为改善CB性能提供了潜在的途径.
相关概念视频
Propagation of Uncertainty from Random Error
661
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...
661
Propagation of Uncertainty from Systematic Error
497
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
497
Sampling Theorem
320
In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
320
Noncompartmental Analysis: Statistical Moment Theory
99
Noncompartmental analyses leverage statistical moment theory to examine time-related changes in macroscopic events, encapsulating the collective outcomes stemming from the constituent elements in play. Statistical moment theory is a mathematical approach used to describe the time course of drug concentration in the body without assuming a specific compartmental model. SMT provides insights into drug absorption, distribution, metabolism, and elimination by treating drug concentration versus time...
99
Sampling Distribution
12.3K
Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
12.3K
Randomized Experiments
6.8K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
Simple randomization
Simple...
6.8K


