Are you a Bayes fan but your model is just not up to the task because with great power comes great computing power consumption as well? Look no further; in this course, we will showcase how one can easily construct a neural network to rapidly emulate summary statistics from almost any complex model. We will also demonstrate how to write a simple Bayesian framework to perform inference with the emulator.
Only basic experience with Python is required. Knowledge about Machine Learning and Bayesian inference is sufficient. Training data and practice codes will be provided for the exercises. Attendees are also encouraged to prepare their own training data. Training data can be anything you would like to emulate of your models, such as a forward-modelled observation that you would like to compare against, or a light cone produced by your 3D simulations. The training will focus on using TensorFlow for network training and EMCEE for Bayesian inference.
The training session is driven on the Australian Research Environment (ARE) and Gadi. Attendees are encouraged to review the following page for background information.