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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
41 stars 9 forks source link

Implement Bayesian Optimization #40

Closed dflemin3 closed 4 years ago

dflemin3 commented 5 years ago

In principle, approxposterior should be able to perform Bayesian optimization, given the GP model, to find maximum likelihood solutions. This is obviously not a new idea and can be implemented using george, e.g. this example, but it would be a good tool to joint the approxposterior ecosystem. For example, one could wish to plot the maximum likelihood estimation on a corner plot of approxposterior constraints, or maybe someone is only interested in the "best fit" example. Either way, it could be a nice addition.

A great algorithm to implement would be Jones+1998.

dflemin3 commented 4 years ago

Finding the maximum a posteriori (MAP) solution, given a GP model, has been implemented.

dflemin3 commented 4 years ago

That is, I implemented approximate Bayesian optimization in #55