scholarly journals From theories to models to predictions: A Bayesian model comparison approach

2017 ◽  
Author(s):  
Jeffrey Rouder ◽  
Julia M. Haaf ◽  
Frederik Aust
Keyword(s):  
Statistical Models ◽  
Bayesian Model ◽  
Model Comparison ◽  
Bayes Factor ◽  
Significance Testing ◽  
Bayesian Model Comparison ◽  
Comparison Approach ◽  
Competing Models ◽  
Competing Hypotheses

A key goal in research is to use data to assess competing hypotheses or theories. Analternative to the conventional significance testing is Bayesian model comparison. The mainidea is that competing theories are represented by statistical models. In the Bayesianframework, these models then yield predictions about data even before the data are seen.How well the data match the predictions under competing models may be calculated, andthe ratio of these matches—the Bayes factor—is used to assess the evidence for one modelcompared to another. We illustrate the process of going from theories to models and topredictions in the context of two hypothetical examples about how exposure to media affectsattitudes toward refugees.

scholarly journals A Bayesian model comparison approach to test the specificity of visual integration impairment in schizophrenia or psychosis

2018 ◽  
Vol 265 ◽  
pp. 271-278 ◽  
Author(s):  
Tyler B. Grove ◽  
Beier Yao ◽  
Savanna A. Mueller ◽  
Merranda McLaughlin ◽  
Vicki L. Ellingrod ◽  
...  
Keyword(s):  
Bayesian Model ◽  
Model Comparison ◽  
Visual Integration ◽  
Bayesian Model Comparison ◽  
Comparison Approach

Uncertainty of prior and posterior model probability: Implications for interpreting Bayes factors

2021 ◽  
Author(s):  
John K. Kruschke
Keyword(s):  
Posterior Distribution ◽  
Bayesian Model ◽  
Model Comparison ◽  
Bayes Factor ◽  
Bayes Factors ◽  
Prior Model ◽  
Bayesian Hypothesis Testing ◽  
Bayesian Model Comparison ◽  
Posterior Model Probability ◽  
Model Probability

In most applications of Bayesian model comparison or Bayesian hypothesis testing, the results are reported in terms of the Bayes factor only, not in terms of the posterior probabilities of the models. Posterior model probabilities are not reported because researchers are reluctant to declare prior model probabilities, which in turn stems from uncertainty in the prior. Fortunately, Bayesian formalisms are designed to embrace prior uncertainty, not ignore it. This article provides a novel derivation of the posterior distribution of model probability, and shows many examples. The posterior distribution is useful for making decisions taking into account the uncertainty of the posterior model probability. Benchmark Bayes factors are provided for a spectrum of priors on model probability. R code is posted at https://osf.io/36527/. This framework and tools will improve interpretation and usefulness of Bayes factors in all their applications.

scholarly journals Capturing Ordinal Theoretical Constraint in Psychological Science

2018 ◽  
Author(s):  
Julia M. Haaf ◽  
Fayette Klaassen ◽  
Jeffrey Rouder
Keyword(s):  
Social Sciences ◽  
Linear Model ◽  
Bayesian Model ◽  
Model Comparison ◽  
Social Scientists ◽  
Psychological Science ◽  
Bayesian Model Comparison ◽  
Comparison Approach ◽  
The Social ◽  
Ordinal Level

Most theories in the social sciences are verbal and provide ordinal-level predictions for data. For example, a theory might predict that performance is better in one condition than another, but not by how much. One way of gaining additional specificity is to posit many ordinal constraints that hold simultaneously. For example a theory might predict an effect in one condition, a larger effect in another, and none in a third. We show how common theoretical positions naturally lead to multiple ordinal constraints. To assess whether multiple ordinal constraints hold in data, we adopt a Bayesian model comparison approach. The result is an inferential system that is custom-tuned for the way social scientists conceptualize theory, and that is more intuitive and informative than current linear-model approaches.

Assessing the relevance of fMRI-based prior in the EEG inverse problem: a bayesian model comparison approach

2005 ◽  
Vol 53 (9) ◽  
pp. 3461-3472 ◽  
Author(s):  
J. Daunizeau ◽  
C. Grova ◽  
J. Mattout ◽  
G. Marrelec ◽  
D. Clonda ◽  
...  
Keyword(s):  
Inverse Problem ◽  
Bayesian Model ◽  
Model Comparison ◽  
Bayesian Model Comparison ◽  
Comparison Approach ◽  
Eeg Inverse Problem

scholarly journals Testing two competing hypotheses for Eurasian jays’ caching for the future: planning versus compensatory caching

2020 ◽  
Author(s):  
Piero Amodio ◽  
Johanni Brea ◽  
Benjamin G. Farrar ◽  
Ljerka Ostojic ◽  
Nicola S. Clayton
Keyword(s):  
Bayesian Model ◽  
Model Comparison ◽  
Future Research ◽  
Future Planning ◽  
The Past ◽  
Bayesian Model Comparison ◽  
The Future ◽  
Competing Hypotheses ◽  
Insight Into ◽  
Specific Food

AbstractPrevious research reported that corvids preferentially cache food in a location where no food will be available or cache more of a specific food in a location where this food will not be available. Here, we consider possible explanations for these prospective caching behaviours and directly compare two competing hypotheses. The Compensatory Caching Hypothesis suggests that birds learn to cache more of a particular food in places where that food was less frequently available in the past. In contrast, the Future Planning Hypothesis suggests that birds recall what-when-where features of specific past events to predict the future availability of food. We designed a protocol in which the two hypotheses predict different caching patterns across different caching locations such that the two explanations can be disambiguated. We formalised the hypotheses in a Bayesian model comparison and tested this protocol in two experiments with one of the previously tested species, namely Eurasian jays. Consistently across the two experiments, the observed caching pattern did not support either hypothesis; rather it was best explained by a uniform distribution of caches over the different caching locations. Future research is needed to gain more insight into the cognitive mechanism underpinning corvids’ caching for the future.

scholarly journals From theories to models to predictions: A Bayesian model comparison approach

2017 ◽  
Vol 85 (1) ◽  
pp. 41-56 ◽  
Author(s):  
Jeffrey N. Rouder ◽  
Julia M. Haaf ◽  
Frederik Aust
Keyword(s):  
Bayesian Model ◽  
Model Comparison ◽  
Bayesian Model Comparison ◽  
Comparison Approach
Sign in / Sign up

Export Citation Format

Share Document