We present a Bayesian-odds-ratio-based algorithm for detecting stellar flares in light curve data. We assume flares are described by a model in which there is a rapid rise with a half-Gaussian profile, followed by an exponential decay. Our signal model also contains a polynomial background model. This is required to fit underlying light curve variations that are expected in the data, which could otherwise partially mimic a flare. We characterise the false alarm probability and efficiency of this method and compare it with a simpler thresholding method based on that used in Walkowicz et al (2011). We find our method has a significant increase in detection efficiency for low signal-to-noise ratio (S/N) flares. For a conservative false alarm probability our method can detect 95% of flares with S/N less than ~20, as compared to S/N of ~25 for the simpler method. As an example we have applied our method to a selection of stars in Kepler Quarter 1 data. The method finds 687 flaring stars with a total of 1873 flares after vetos have been applied. For these flares we have characterised their durations and and signal-to-noise ratios.
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