Sample-efficient neural likelihood-free Bayesian inference of implicit HMMs

This repository contains code that demonstrate the application of a Masked Autoregressive Flow to estimate the hidden states of an implicit Hidden Markov Model, supporting the above paper, published in AISTATS 2024.

Dependencies

We are using the sbi package heavily. See the requirements.txt

Usage

Compile the c++ code

The base simulators for both the Lotka-Volterra and the Prokaryotic models are implemented inC++. These need compilation.

Go to ./CPP directory and then compile by using python setup.py build_ext -i.

Then rename the generated *.so files to lvssa.so and pkyssa.so.

To run the stochastic Lotka-Volterra example model: python lotkavolterra_nlfi.py

This will run sequential NLFI to infer the posterior parameters and IDE (and SMC) for hidden states. Also, run:

python lotkavolterra_abc.py for infering the states using ABC-SMC. Once these are finished, run

python lotkavolterra_plots.py for visualisation.

Follow the above for the Prokaryotic model. For running the Nonlinear Gaussian statespace model:

python statespace.py