bridgesampling: An R Package for Estimating Normalizing Constants

Gronau, Quentin Frederik; Singmann, Henrik; Wagenmakers, Eric‐Jan

doi:10.31222/osf.io/v94h6

Cited by 108 publications

(102 citation statements)

References 39 publications

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“…We fit our data to an extended MWC-type model including an additional free parameter (C), representing negative binding cooperativity between subunits (Figure 2-Figure supplement 2). Not surprisingly this model improved the fit to our data as assessed by the Bayes factor, which represents the marginal likelihood of one model over another to explain our observations (Wagenmakers, 2007;Gronau et al, 2017). We also tested the cooperative model using approximate leave-one-out cross validation, which assesses the ability of a model to predict new or out-of-sample data using in-sample fits.…”

Section: Discussionmentioning

confidence: 89%

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Nucleotide inhibition of the pancreatic ATP-sensitive K+ channel explored with patch-clamp fluorometry

Usher

Ashcroft

Puljung

2019

Preprint

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Pancreatic ATP-sensitive K + channels (KATP) comprise four inward rectifier subunits (Kir6.2), each associated with a sulphonylurea receptor (SUR1). ATP/ADP binding to Kir6.2 shuts KATP.Mg-nucleotide binding to SUR1 stimulates KATP. In the absence of Mg 2+ , SUR1 increases the apparent affinity for nucleotide inhibition at Kir6.2 by an unknown mechanism. We simultaneously measured channel currents and nucleotide binding to Kir6.2. Fits to combined data sets suggest that KATP closes with only one nucleotide molecule bound. A Kir6.2 mutation (C166S) that increases channel activity did not affect nucleotide binding, but greatly perturbed the ability of bound nucleotide to inhibit KATP. Mutations at position K205 in SUR1 affected both nucleotide affinity and the ability of bound nucleotide to inhibit KATP. This suggests a dual role for SUR1 in KATP inhibition, both in directly contributing to nucleotide binding and in stabilising the nucleotidebound closed state.

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Section: Discussionmentioning

confidence: 89%

“…Where applicable, the posterior probabilities of each parameter are reported as the median and the 95% equal-tailed interval. Bayes factors were calculated using bridgesampling (Gronau et al, 2017), and Leave-One-Out (LOO) cross-validation was performed using the loo package (Vehtari et al, 2017).…”

Section: Surface Expression Assaysmentioning

confidence: 99%

Nucleotide inhibition of the pancreatic ATP-sensitive K+ channel explored with patch-clamp fluorometry

Usher

Ashcroft

Puljung

2019

Preprint

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“…In this specific case, the constrained EVSDT model was represented by the null hypothesis H 0 stating that the group-level σ V σ I can take a small range of values, between .99 and 1.01, and an encompassing alternative hypothesis H A that imposed no such constraint. 10 In typical settings, the use of Bayes Factors requires the computation of marginal likelihoods for (at least) two models, which can be quite challenging (but see Gronau et al, 2017). But in this specific case in which the hypotheses considered consist of nested ranges of admissible parameter values (specifically, the range of σ V σ I ), Bayes Factors can be easily computed.…”

Section: Resultsmentioning

confidence: 99%

Characterizing Belief Bias in Syllogistic Reasoning: A Hierarchical-Bayesian Meta-Analysis of ROC Data

Trippas¹,

Kellen²,

Singmann³

et al. 2017

Preprint

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The belief-bias effect is one of the most-studied biases in reasoning. A recent study of the phenomenon using the signal detection theory (SDT) model called into question all theoretical accounts of belief bias by demonstrating that beliefbased differences in the ability to discriminate between valid and invalid syllogisms may be an artifact stemming from the use of inappropriate linear measurement models such as analysis of variance (Dube et al., Psychological Review, 117 (3), 2010). The discrepancy between Dube et al. 's, Psychological Review, 117(3), 831-863 (2010) results and the previous three decades of work, together with former's methodological criticisms suggests the need to revisit earlier results, this time collecting confidence-rating responses. Using a hierarchical Bayesian meta-analysis, we reanalyzed a corpus of 22 confidence-rating studies (N = 993). The results indicated that extensive replications using confidence-rating data are unnecessary as the observed receiver operating characteristic functions are not systematically asymmetric. These results were subsequently corroborated by a novel experimental design based on SDT's generalized area theorem. Although the metaanalysis confirms that believability does not influence discriminability unconditionally, it also confirmed previous results that factors such as individual differences mediate the effect. The main point is that data from previous and future studies can be safely analyzed using appropriate hierarchical methods that do not require confidence ratings. More generally, our results set a new standard for analyzing data and evaluating theories in reasoning. Important methodological and theoretical considerations for future work on belief bias and related domains are discussed.

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“…As a fourth method, the marginal likelihoods in equation were approximated directly using warp‐III bridge sampling (Gronau, Wagenmakers, Heck, & Matzke, in press; Meng & Schilling, ). This method is available via the R package bridgesampling (Gronau, Singmann, & Wagenmakers, ), which only requires the fitted Stan objects of the nested and full model to approximate the Bayes factor. The R code to replicate all analyses is available in the supplementary material at the Open Science Framework (https://osf.io/5hpuc/).…”

Section: Computing Bayes Factors For Regression Parametersmentioning

confidence: 99%

A caveat on the Savage–Dickey density ratio: The case of computing Bayes factors for regression parameters

Heck

2018

Brit J Math & Statis

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The Savage-Dickey density ratio is a simple method for computing the Bayes factor for an equality constraint on one or more parameters of a statistical model. In regression analysis, this includes the important scenario of testing whether one or more of the covariates have an effect on the dependent variable. However, the Savage-Dickey ratio only provides the correct Bayes factor if the prior distribution of the nuisance parameters under the nested model is identical to the conditional prior under the full model given the equality constraint. This condition is violated for multiple regression models with a Jeffreys-Zellner-Siow prior, which is often used as a default prior in psychology. Besides linear regression models, the limitation of the Savage-Dickey ratio is especially relevant when analytical solutions for the Bayes factor are not available. This is the case for generalized linear models, non-linear models, or cognitive process models with regression extensions. As a remedy, the correct Bayes factor can be computed using a generalized version of the Savage-Dickey density ratio.

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bridgesampling: An R Package for Estimating Normalizing Constants

Cited by 108 publications

References 39 publications

Nucleotide inhibition of the pancreatic ATP-sensitive K+ channel explored with patch-clamp fluorometry

Nucleotide inhibition of the pancreatic ATP-sensitive K+ channel explored with patch-clamp fluorometry

Characterizing Belief Bias in Syllogistic Reasoning: A Hierarchical-Bayesian Meta-Analysis of ROC Data

A caveat on the Savage–Dickey density ratio: The case of computing Bayes factors for regression parameters

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