Comments (2)
Accidentally closed the wrong issue!
from approxposterior.
This task can be accomplished in a few ways:
-
k-folds cross-validation
At any point in the algorithm, I can partition the training set, {theta, y}, into k folds, training on k-1 folds and predicting on the kth fold. This procedure yields a pretty decent estimate of the the accuracy and uncertainty of the predictions that can be compared to the GP's own uncertainty from its conditional predictive distribution. -
on-the-fly
Before the GP evaluates the forward model, I can use it to predict the functional value at that point, with uncertainties under the GP model, to assess the accuracy of the prediction.
Both of these methods should be implemented, and both should not be too computationally-expensive as re-training the GP for a given set of hyperparameters is decently quick with george given that be design, our training set sizes are small.
from approxposterior.
Related Issues (20)
- Optimize the GP less HOT 1
- conda installation is broke HOT 2
- Multiprocessing is slow: too much overhead spinning up new processes HOT 2
- Let users set the name of the output files HOT 1
- Scaling parameter values to improve GP hyperparameter optimization HOT 1
- Use other regression algorithms besides the GP for logprobability predictions HOT 1
- Implement Bayesian Optimization HOT 2
- Cross-Validation to select GP hyperparameters HOT 1
- Explore approxposterior parallelization paradigm HOT 1
- Utility functions for training set initialization HOT 1
- Add bounds, scale to ApproxPosterior object? HOT 1
- Can't clone from [email protected]:dflemin3 HOT 1
- Use latin hypercube sampler to initialize GP optimizations HOT 1
- Add MultiNest for posterior retreival HOT 1
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- Add a warning for when the GP optimization optGPEveryN > m HOT 1
- Standardize code formatting
- Don't use nbsphinx_prompt_width to hide prompts HOT 1
- Single parameter inference causes ValueError
- Unnecessary creation of a new GP object in `findNextPoint`
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from approxposterior.