Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC
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Summary
An efficient computation of LOO is introduced using Pareto-smoothed importance sampling (PSIS), a new procedure for regularizing importance weights, and it is demonstrated that PSIS-LOO is more robust in the finite case with weak priors or influential observations.
- Type
- article
- Published
- 2015-07-16
- Cited by
- 5,199
- References
- 47
- Access
- Open access
- OpenAlex
- https://openalex.org/W2203714058
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:36568061
Keywords
Cross-validation, Bayesian probability, Computer science, Model validation, Variable-order Bayesian network
References
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- A survey of cross-validation procedures for model selection
- DIC in variable selection
- A survey of Bayesian predictive methods for model assessment, selection and comparison
- Strictly Proper Scoring Rules, Prediction, and Estimation
- On the variability of case-deletion importance sampling weights in the Bayesian linear model
- Predictive likelihood for Bayesian model selection and averaging
- Sequential Imputations and Bayesian Missing Data Problems
- Truncated Importance Sampling
- Estimation in Parallel Randomized Experiments
- Bayesian Model Assessment and Comparison Using Cross-Validation Predictive Densities
- Testing the assumptions behind importance sampling
- The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo
- Understanding predictive information criteria for Bayesian models
- A comparative study of ordinary cross-validation, v-fold cross-validation and the repeated learning-testing methods
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- Pareto Smoothed Importance Sampling
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- When Too Many Vowels Impede Language Processing: An Eye-Tracking Study of Danish-Learning Children
- Estimating cross‐validatory predictive p‐values with integrated importance sampling for disease mapping models
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