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lgpr: An interpretable nonparametric method for inferring covariate effects from longitudinal data
Juho Timonen, Henrik Mannerström, Aki Vehtari +1
Longitudinal study designs are indispensable for studying disease progression. Inferring covariate effects from longitudinal data, however, requires interpretable methods that can…
Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation
Marko Järvenpää, Aki Vehtari, Pekka Marttinen
The computational efficiency of approximate Bayesian computation (ABC) has been improved by using surrogate models such as Gaussian processes (GP). In one such promising framework…
Parallel Gaussian process surrogate Bayesian inference with noisy likelihood evaluations
Marko Järvenpää, Michael Gutmann, Aki Vehtari +1
We consider Bayesian inference when only a limited number of noisy log-likelihood evaluations can be obtained. This occurs for example when complex simulator-based statistical mode…
Bayesian leave-one-out cross-validation for large data
Måns Magnusson, Michael Riis Andersen, Johan Jonasson +1
Model inference, such as model comparison, model checking, and model selection, is an important part of model development. Leave-one-out cross-validation (LOO) is a general approac…
Active Learning for Decision-Making from Imbalanced Observational Data
Iiris Sundin, Peter Schulam, Eero Siivola +3
Machine learning can help personalized decision support by learning models to predict individual treatment effects (ITE). This work studies the reliability of prediction-based deci…