Learn how to do inference in a higher order probabilistic programming language in a way that is very near to being compatible with tight-bounds model-learning via gradient-based evidence lower-bounding. In particular you are going to learn how to do sequential Monte-Carlo-based inference in the probabilistic programming context. You should note in doing this homework the increased efficiency of inference in the HMM model in particular, the model in which partial "reward" is accumulated.
Note: This code base was developed on Python3.7
Clone Daphne directly into this repo:
git clone git@github.com:plai-group/daphne.git(To use Daphne you will need to have both a JVM installed and Leiningen installed)
pip3 install -r requirements.txtChange the daphne path in src/smc.py and src/daphne.py run:
cd src
python3 smc.py