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Related Concept Videos

Sampling Methods: Overview01:06

Sampling Methods: Overview

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A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of...
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Stratified Sampling Method01:16

Stratified Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...
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Cluster Sampling Method01:20

Cluster Sampling Method

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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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Systematic Sampling Method01:17

Systematic Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
Systematic sampling is one of the simplest methods...
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Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

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Sampling materials are classified into three main types: solid, liquid, and gas.
Solid samples include a variety of substances, such as sediments from water bodies, soil, metals, and biological tissues. Two standard methods for extracting sediments from water bodies are grab sampling and piston coring. Grab sampling involves using a device to collect a discrete sediment sample from the bottom of a water body with minimal disturbance. Grab samples do not always represent the entire area due to...
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Sampling Plans01:23

Sampling Plans

333
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
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IntelligentPooling: Practical Thompson Sampling for mHealth.

Sabina Tomkins1, Peng Liao2, Predrag Klasnja3

  • 1Stanford University.

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|October 8, 2021
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Summary
This summary is machine-generated.

IntelligentPooling enhances mobile health (mHealth) by personalizing behavioral treatments using reinforcement learning. This approach addresses individual response differences, limited data, and changing user needs for better health outcomes.

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Area of Science:

  • Digital Health
  • Machine Learning in Healthcare
  • Behavioral Science

Background:

  • Mobile health (mHealth) utilizes smart devices for repeated behavioral interventions to promote healthy habits.
  • Reinforcement learning (RL) is a promising approach for optimizing sequential treatment decisions in mHealth.
  • Current RL applications face challenges including individual response variability, limited user data, and non-stationary treatment effects.

Purpose of the Study:

  • To develop a novel reinforcement learning framework, IntelligentPooling, to address key challenges in personalized mHealth behavioral interventions.
  • To create a system capable of learning personalized treatment policies that account for individual differences in response.
  • To enhance learning efficiency with limited individual data by leveraging data from other users.

Main Methods:

  • Generalizing Thompson-Sampling bandit algorithms to create the IntelligentPooling framework.
  • Implementing personalized treatment policies by adapting to individual user contexts.
  • Developing a mechanism for updating personalization levels and utilizing pooled data for faster learning.
  • Incorporating time-varying responsivity to accommodate non-stationary treatment effects.

Main Results:

  • IntelligentPooling effectively learns personalized treatment policies, addressing differential individual responses.
  • The method accelerates learning for individual users by intelligently pooling data from a larger user base.
  • The framework successfully adapts to non-stationary treatment responses, adjusting based on the duration of user engagement.

Conclusions:

  • IntelligentPooling offers a robust solution for deploying reinforcement learning in mHealth by tackling personalization, data scarcity, and non-stationarity.
  • This approach holds significant potential for optimizing digital behavioral interventions and improving user health outcomes.
  • Further research can explore the scalability and real-world clinical utility of IntelligentPooling across diverse mHealth applications.