Bayesian parameter estimation using stochastic sampling

Plan

1. Demonstration of Nested Sampling
2. Basic straight line fit and model comparison (or your own likelihood)
3. BBH 4 parameter tutorial
4. BNS 4 parameter tutorial with tidal deformabilities
5. Hierarchical inference of mass distribution using GWTC-3 events

Setup

Using PC (recommended)

1. Use the environment.yaml file in nbs directory and anaconda/miniconda:

conda env create -f environment.yaml

1. Activate the environment

conda activate np3m

Using Google Collab

1. Access the notebooks here
2. Execute the approriate cell to install the requisite packages.

References

1. pycbc for generating waveforms, bayesian inference PE on GW events.
2. bilby A multi sampler equipped Bayesian PE tool, primarily developed for GW inference.
3. gwpopupation A python package to carry out Bayesian Hierarchical inference to infer population properties of CBC events.
4. dynesty A dynamic nested monte carlo sampler.