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A binomial distribution is a probability distribution for a procedure with a fixed number of trials, where each trial can have only two outcomes.
The outcomes of a binomial experiment fit a binomial probability distribution. A statistical experiment can be classified as a binomial experiment if the following conditions are met:
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A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
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A complete procedure for testing a claim about a population proportion is provided here.
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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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Obtaining Well Calibrated Probabilities Using Bayesian Binning.

Mahdi Pakdaman Naeini1, Gregory F Cooper2, Milos Hauskrecht3

  • 1Intelligent Systems Program, University of Pittsburgh, PA, USA.

Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence
|May 1, 2015
PubMed
Summary
This summary is machine-generated.

We introduce Bayesian Binning into Quantiles (BBQ), a novel method for improving the calibration of AI predictive models. BBQ enhances model reliability for critical decision-making tasks by post-processing classification outputs.

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Probabilistic Modeling

Background:

  • Well-calibrated probabilistic predictive models are essential for reliable AI decision-making.
  • Existing calibration methods have limitations that hinder their widespread application.

Purpose of the Study:

  • To introduce a new non-parametric calibration method, Bayesian Binning into Quantiles (BBQ).
  • To address the key limitations of current calibration techniques in AI.

Main Methods:

  • Developed a post-processing technique applicable to binary classification algorithms.
  • Ensured the method is computationally tractable for practical use.

Main Results:

  • Demonstrated empirical accuracy through experiments on real and simulated datasets.
  • BBQ can be readily integrated with various existing classification algorithms.

Conclusions:

  • Bayesian Binning into Quantiles (BBQ) offers an effective and practical solution for improving model calibration.
  • The proposed method enhances the reliability of AI predictions for critical applications.