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dflemin3 / approxposterior

A Python package for approximate Bayesian inference and optimization using Gaussian processes
https://dflemin3.github.io/approxposterior/
MIT License
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Utility functions for training set initialization #43

Closed dflemin3 closed 5 years ago

dflemin3 commented 5 years ago

I should add utility functions to help users initialize their training sets. That is, given hard bounds for parameter ranges, functions that generate sets of initial conditions for the user's foward model to build the initial GP training set. Options will include sampling from the prior (already implemented), uniform over the ranges, and Latin hypercube sampling as was used in this paper and this paper by Simeon Bird and collaborators. The latin hypercube sampling option can be particularly useful, it seems.

After implementing these utility functions, I should add tests and documentation/examples.

dflemin3 commented 5 years ago

Implemented in the dev branch using the pyDOE latin hypercube sampling implementation, but with the user able to scale by the parameter bounds of their choosing.