active_learning.rst
Active Learning
Active learning is a machine learning technique that involves selecting the most informative data points for the purpose of training an emulator. The idea behind active learning is to reduce the amount of training data required for a machine learning model to achieve a certain level of accuracy. This is achieved by iteratively choosing a new data point that is expected to be the most informative.
In this module, the :class:`.ActiveLearning` class is implemented to actively build a Gaussian process emulator for the natural logarithm of the unnormalized posterior in Bayesian inference. It is supposed to facilitate efficient parameter calibration of computationally expensive simulators. For detailed theories, please refer to :cite:t:`Wang2018`, :cite:t:`Kandasamy2017`, and :cite:t:`Zhao2022`.
ActiveLearning Class
The :class:`.ActiveLearning` class is imported by:
from psimpy.inference.active_learning import ActiveLearning