Rejecting or Accepting Parameter Values in Bayesian Estimation

By John K. Kruschke. 2018

662 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/bb953904481df733edf37d38200596a43fcd67e7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Mattan S. Ben-Shachar
2. Daniel Lüdecke
3. Dominique Makowski
4. Indrajeet Patil
5. Daniël Lakens

In Statistics and Probability

1. Mattan S. Ben-Shachar
2. Daniel Lüdecke
3. Dominique Makowski
4. Daniël Lakens
5. Indrajeet Patil
6. Farid Anvari
7. C. Brydges
8. Christiany Suwartono

Citations in the corpus

• bayestestR: Describing Effects and their Uncertainty, Existence and Significance within the Bayesian Framework (2019, methodology): “Such a Test for Practical Equivalence , implemented via equivalence_test() , is based on the “HDI+ROPE decision rule” (Kruschke, 2018) to check whether parameter values should be accepted or rejected against an explicitly formulated “null hypothesis” (i.e., a ROPE).”
• Sample Size Justification (2021, positive): “Morey (2020) convincingly argues that most practical use-cases of planning for precision involve an inferential goal of distinguishing an observed effect from other effect sizes (for a Bayesian perspective, see Kruschke (2018)).”