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We describe a simple and efficient Python code to perform Bayesianforecasting for gravitational waves (GW) produced byExtreme-Mass-Ratio-Inspiral systems (EMRIs). The code runs on GPUs for anefficient parallelised computation of thousands of waveforms and sampling ofthe posterior through a Markov-Chain-Monte-Carlo (MCMC) algorithm. EMRI_MCgenerates EMRI waveforms based on the so--called kludge scheme, and propagatesit to the observer accounting for cosmological effects in the observed waveformdue to modified gravity/dark energy. Extending the code to more accurateschemes for the generation of the waveform is straightforward. Despite theknown limitations of the kludge formalism, we believe that the code can providea helpful resource for the community working on forecasts for interferometrymissions in the milli-Hz scale, predominantly, the satellite-mission LISA.

EMRI_MC: A GPU-based code for Bayesian inference of EMRI waveforms