The last 25 years have shown a steady increase in attention for the Bayes factor as a tool for
hypothesis evaluation and model selection. The present review highlights the potential of …

Eliciting informative prior distributions for Bayesian inference can often be complex and
challenging. Although popular methods rely on asking experts probability-based questions …

Prior Knowledge Elicitation: The Past, Present, and Future Page 1 Bayesian Analysis (2024)
19, Number 4, pp. 1129–1161 Prior Knowledge Elicitation: The Past, Present, and Future ∗ …

Over the past decade, the idiographic approach has received significant attention in clinical
psychology, incentivizing the development of novel approaches to estimate statistical …

A central characteristic of Bayesian statistics is the ability to consistently incorporate prior
knowledge into various modeling processes. In this paper, we focus on translating domain …

Personalized networks of psychological symptoms aim to advance therapy by identifying
treatment targets for specific patients. Statistical relations in such networks can be estimated …

Tendeiro and Kiers (2019) provide a detailed and scholarly critique of Null Hypothesis
Bayesian Testing (NHBT) and its central component—the Bayes factor—that allows …

Bayesian inference requires the specification of prior distributions that quantify the pre-data
uncertainty about parameter values. One way to specify prior distributions is through prior …

Background We provide an overview of Bayesian estimation, hypothesis testing, and model-
averaging and illustrate how they benefit parametric survival analysis. We contrast the …

People exhibit scope insensitivity: Their expressed valuation of a problem is not
proportionate with its scope or size. To address scope insensitivity in charitable giving, Hsee …