Estimation of drift and diffusion functions from unevenly sampled time-series data

Complex systems can often be modeled as stochastic processes. However, physical observations of such systems are often irregularly spaced in time, leading to difficulties in estimating appropriate models from data. Here we present extensions of two methods for estimating drift and diffusion functions from irregularly sampled time-series data. Our methods are flexible and applicable to a variety of stochastic systems, including non-Markov processes or systems contaminated with measurement noise. To demonstrate applicability, we use this approach to analyze an irregularly sampled paleoclimatological isotope record, giving insights into underlying physical processes.