An increasingly louder gravitational wave sky brings about a host of data analysis challenges
especially when it comes to parameter inference. It is well understood that direct implementation of traditional, likelihood-based inference techniques such as e.g. dynesty, MCMC etc. for parameter inference of next-generation gravitational wave signals will not be feasible or even
possible in certain cases where the likelihood function becomes mathematically intractable. In
this article, I propose an implicit-likelihood technique called sequential simulation based inference packaged within the open-source pipeline PEREGRINE and its applicability in dealing with
upcoming data analysis challenges in gravitational wave physics. I highlight the simulation efficiency that peregrine exhibits whilst ensuring optimal precision in a statistically robust way.
Ultimately, I emphasize the potential of implicit-likelihood techniques for parameter inference of
multiple different types of signals in the current and upcoming era of gravitational wave physics.