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The Laser Interferometer Space Antenna (LISA) is scheduled to launch in themid 2030s, and is expected to observe gravitational-wave candidates frommassive black-hole binary mergers, extreme mass-ratio inspirals, and more.Accurately inferring the source properties from the observed gravitational-wavesignals is crucial to maximise the scientific return of the LISA mission.BILBY, the user-friendly Bayesian inference library, is regularly used forperforming gravitational-wave inference on data from existing ground-basedgravitational-wave detectors. Given that Bayesian inference with LISA includesadditional subtitles and complexities beyond it's ground-based counterpart, inthis work we modify BILBY to perform parameter estimation with LISA. We showthat full nested sampling can be performed to accurately infer the propertiesof LISA sources from transient gravitational-wave signals in a) zero-noise andb) idealized instrumental noise. By focusing on massive black-hole binarymergers, we demonstrate that higher order multipole waveform models can be usedto analyse a year's worth of simulated LISA data, and discuss the computationalcost and performance of full nested sampling compared with techniques foroptimising likelihood calculations, such as the heterodyned likelihood.

BILBY in space: Bayesian inference for transient gravitational-wave signals observed with LISA