Code and plots for the paper "Cosmological Parameter Estimation with Sequential Linear Simulation-based Inference" by N. G. Mediato-Diaz and W. J. Handley.
Web reference: arXiv: 2501.03921
Project page: Galileo - Part III Projects supervised by Will Handley
We develop the framework of Linear Simulation-based Inference (LSBI), an application of simulation-based inference where the likelihood is approximated by a Gaussian linear function of its parameters. We obtain analytical expressions for the posterior distributions of hyper-parameters of the linear likelihood in terms of samples drawn from a simulator, for both uniform and conjugate priors. This method is applied sequentially to several toy-models and tested on emulated datasets for the Cosmic Microwave Background temperature power spectrum. We find that convergence is achieved after four or five rounds of
- Python 3.9+
- lsbi - GitHub, Documenation
- globalemu - GitHub, Documenation
- cmbemu - GitHub
- cosmopower-jax - GitHub
- dynesty - GitHub, Documentation
- getdist - GitHub, Documentation
Handley et al, (2024) lsbi: Linear Simulation Based Inference.
Bevins, H., Handley, W. J., Fialkov, A., Acedo, E. D. L., and Javid, K. (2021). GLOBALEMU: A novel and robust approach for emulating the sky-averaged 21-cm signal from the cosmic dawn and epoch of reionisation. arXiv:2104.04336
Piras, D. and Mancini, A.S. (2023). CosmoPower-JAX: high-dimensional Bayesian inference with differentiable cosmological emulators. arXiv preprint arXiv:2305.06347.
Speagle, J.S. (2020). dynesty: a dynamic nested sampling package for estimating Bayesian posteriors and evidences. Monthly Notices of the Royal Astronomical Society, 493(3), pp.3132-3158.
Lewis, A. (2019). GetDist: a Python package for analysing Monte Carlo samples. arXiv preprint arXiv:1910.13970.