Toward a principled Bayesian workflow in cognitive science.
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Summary
A principled Bayesian workflow is introduced that provides guidelines and checks for valid data analysis, avoiding overfitting complex models to noise, and capturing relevant data structure in a probabilistic model.
- Type
- preprint
- Published
- 2019-04-29
- Cited by
- 217
- References
- 55
- Access
- Open access
- OpenAlex
- https://openalex.org/W2941987401
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:139106937
Keywords
Computer science, Workflow, Bayesian probability, Overfitting, Artificial intelligence
References
- Bayesian Cognitive Modeling: A Practical Course
- Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan
- WinBUGS - A Bayesian modelling framework: Concepts, structure, and extensibility
- On thinning of chains in MCMC
- Fitting Linear Mixed-Effects Models Using lme4
- Processing Chinese Relative Clauses: Evidence for the Subject-Relative Advantage
- The deviance information criterion: 12 years on
- Mixed-Effects Models in S and S-Plus
- How cognitive modeling can benefit from hierarchical Bayesian models.
- Probability and the Weighing of Evidence
- Processing Chinese relative clauses in context
- A STATISTICAL PARADOX
- Hierarchical single- and dual-process models of recognition memory
- Validation of Software for Bayesian Models Using Posterior Quantiles
- Hierarchical Bayesian parameter estimation for cumulative prospect theory
- Generating random correlation matrices based on vines and extended onion method
- Mixed-effects modeling with crossed random effects for subjects and items
- Understanding memory impairment with memory models and hierarchical Bayesian analysis
- Random effects structure for confirmatory hypothesis testing: Keep it maximal
- A Bayesian hierarchical model for the measurement of working memory capacity
Cited by
- The Experiment is just as Important as the Likelihood in Understanding the Prior: a Cautionary Note on Robust Cognitive Modeling
- Know your population and know your model: Using model-based regression and post-stratification to generalize findings beyond the observed sample
- Pragmatic constraints do not prevent the co-activation of alternative names: evidence from sequential naming tasks with one and two speakers
- The Really Risky Registered Modeling Report: Incentivizing Strong Tests and HONEST Modeling in Cognitive Science
- Prior specification via prior predictive matching: Poisson matrix factorization and beyond
- Flexible Prior Elicitation via the Prior Predictive Distribution
- Behavioral Software Engineering: Methodological Introduction to Psychometrics
- Psychometrics in Behavioral Software Engineering: A Methodological Introduction with Guidelines
- An empirical study of Linespots: A novel past‐fault algorithm
- Moving Beyond Ordinary Factor Analysis in Studies of Personality and Personality Disorder: A Computational Modeling Perspective
- Getting to the heart of it: Multi-method exploration of nonconscious prioritization processes.
- Noisy is better than rare: Comprehenders compromise subject-verb agreement to form more probable linguistic structures.
- Choosing priors in Bayesian ecological models by simulating from the prior predictive distribution
- Encoding interference effects support self-organized sentence processing.
- A Principled Approach to Feature Selection in Models of Sentence Processing
- Systematic Parameter Reviews in Cognitive Modeling: Towards a Robust and Cumulative Characterization of Psychological Processes in the Diffusion Decision Model
- Applying Bayesian Analysis Guidelines to Empirical Software Engineering Data: The Case of Programming Languages and Code Quality
- ベイズ統計モデリングの有用性を示す認知心理学研究の紹介：個人間・試行間のばらつきを理解する
- Concord begets concord: A Bayesian model of nominal concord typology
- multiverse: Multiplexing Alternative Data Analyses in R Notebooks
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