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Multibridge: an R package to evaluate informed hypotheses in binomial and multinomial models.

Alexandra Sarafoglou1, Frederik Aust2, Maarten Marsman2

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Summary
This summary is machine-generated.

The multibridge R package enables Bayesian evaluation of complex hypotheses using frequency data. It efficiently computes Bayes factors for various equality and inequality constraints in statistical models.

Keywords:
Bayes factorsBridge samplingInequality constraintsModel selectionSavage-Dickey density ratio

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Area of Science:

  • Statistics
  • Computational Statistics

Background:

  • Bayesian inference is crucial for evaluating complex statistical hypotheses.
  • Comparing models with restricted parameter spaces presents computational challenges.

Purpose of the Study:

  • To introduce the multibridge R package for Bayesian hypothesis evaluation.
  • To provide a method for computing Bayes factors for informed hypotheses with various constraints.

Main Methods:

  • Utilizes bridge sampling for efficient computation of Bayes factors.
  • Applies to frequency data from binomial or multinomial distributions.
  • Handles equality, inequality, and combined constraints on latent category proportions.

Main Results:

  • Multibridge facilitates fast and accurate comparison of large, constrained models.
  • Efficiently handles models with limited posterior mass in restricted spaces.
  • Provides a robust framework for Bayesian model comparison.

Conclusions:

  • The multibridge package offers a powerful tool for Bayesian hypothesis testing.
  • Enables flexible and efficient evaluation of complex statistical models.
  • Supports reproducible research through illustrative examples.