Abstract
We consider single-molecule fluorescence experiments with data in the form of counts of photons registered over multiple time-intervals. Based on the observation schemes, linking back to works by Dehmelt [Bull. Am. Phys. Soc. 20 (1975) 60] and Cook and Kimble [Phys. Rev. Lett. 54 (1985) 1023], we propose an analytical approach to the data based on the theory of Markov-modulated Poisson processes (MMPP). In particular, we consider maximum-likelihood estimation. The method is illustrated using a real-life dataset. Additionally, the properties of the proposed method are investigated through simulations and compared to two other approaches developed by Yip et al. [J. Phys. Chem. A 102 (1998) 7564] and Molski [Chem. Phys. Lett. 324 (2000) 301].
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