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We present the publicly available, open source code UCLCHEMCMC, designed toestimate physical parameters of an observed cloud of gas by combining MonteCarlo Markov Chain (MCMC) sampling with chemical and radiative transfermodeling. When given the observed values of different emission lines,UCLCHEMCMC runs a Bayesian parameter inference, using a MCMC algorithm tosample the likelihood and produce an estimate of the posterior probabilitydistribution of the parameters. UCLCHEMCMC takes a full forward modelingapproach, generating model observables from the physical parameters viachemical and radiative transfer modeling. While running UCLCHEMCMC, the createdchemical models and radiative transfer code results are stored in an SQLdatabase, preventing redundant model calculations in future inferences. Thismeans that the more UCLCHEMCMC is used, the more efficient it becomes. UsingUCLCHEM and RADEX, the increase of efficiency is nearly two orders ofmagnitude, going from 5185.33 \pm 1041.96 s for ten walkers to take onethousand steps when the database is empty, to 68.89 \pm 45.39 s when nearly allmodels requested are in the database. In order to demonstrate its usefulness weprovide an example inference of UCLCHEMCMC to estimate the physical parametersof mock data, and perform two inferences on the well studied prestellar core,L1544, one of which show that it is important to consider the substructures ofan object when determining which emission lines to use.

UCLCHEMCMC: A MCMC Inference tool for Physical Parameters of Molecular Clouds