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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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A heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. Different types of heuristics are used in different types of situations, and the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):
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Hindsight Biases01:12

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Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Can you relate this to the phrase "Hindsight is 20/20" now? 
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End Point Prediction: Gran Plot01:07

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A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
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Observational Learning01:12

Observational Learning

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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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Predicting Reaction Outcomes02:24

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Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
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Related Experiment Video

Updated: Aug 20, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

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Time-Aware Explainable Recommendation via Updating Enabled Online Prediction.

Tianming Jiang1, Jiangfeng Zeng1

  • 1School of Information Management, Central China Normal University, Wuhan 430079, China.

Entropy (Basel, Switzerland)
|November 24, 2022
PubMed
Summary

This study introduces a novel framework for explainable recommendation systems that addresses issues with outdated models. The proposed online prediction and updating strategies ensure accurate, timely, and understandable recommendations over time.

Keywords:
data leakageexplainable recommendationmodel agingmodel updatingonline prediction

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Explainable recommendation systems aim to provide accurate predictions and intuitive user explanations.
  • Existing offline methods suffer from data leakage and model aging due to static training and evolving user preferences.

Purpose of the Study:

  • To propose an updating-enabled online prediction framework for time-aware explainable recommendation.
  • To address data leakage and model aging issues inherent in current recommendation system methodologies.

Main Methods:

  • Developed an online prediction scheme to prevent data leakage during training.
  • Introduced two novel updating strategies to mitigate the model aging problem.
  • Conducted extensive experiments on four real-world datasets to validate the framework's effectiveness.

Main Results:

  • The proposed time-aware approach significantly improves recommendation accuracy.
  • The framework provides more convincing explanations compared to state-of-the-art methods.
  • Effectiveness demonstrated across both initial and long-term usage periods.

Conclusions:

  • The developed framework offers a robust solution for dynamic and evolving user-item interactions.
  • This approach enhances the overall performance and reliability of explainable recommendation systems.
  • It ensures high-quality, explainable recommendations throughout the system's lifetime.