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In order to obtain robust cosmological constraints from Type Ia supernova (SNIa) data, we have applied Markov Chain Monte Carlo (MCMC) to SN Ia lightcurvefitting. We develop a method for sampling the resultant probability densitydistributions (pdf) of the SN Ia lightcuve parameters in the MCMC likelihoodanalysis to constrain cosmological parameters, and validate it using simulateddata sets. Applying this method to the Joint Lightcurve Analysis (JLA) data setof SNe Ia, we find that sampling the SN Ia lightcurve parameter pdf's leads tocosmological parameters closer to that of a flat Universe with a cosmologicalconstant, compared to the usual practice of using only the best fit values ofthe SN Ia lightcurve parameters. Our method will be useful in the use of SN Iadata for precision cosmology.

Sampling the Probability Distribution of Type Ia Supernova Lightcurve Parameters in Cosmological Analysis