  In the era of wide-field surveys like the Zwicky Transient Facility and the
Rubin Observatory's Legacy Survey of Space and Time, sparse photometric
measurements constitute an increasing percentage of asteroid observations,
particularly for asteroids newly discovered in these large surveys. Follow-up
observations to supplement these sparse data may be prohibitively expensive in
many cases, so to overcome these sampling limitations, we introduce a flexible
model based on Gaussian Processes to enable Bayesian parameter inference of
asteroid time series data. This model is designed to be flexible and
extensible, and can model multiple asteroid properties such as the rotation
period, light curve amplitude, changing pulse profile, and magnitude changes
due to the phase angle evolution at the same time. Here, we focus on the
inference of rotation periods. Based on both simulated light curves and real
observations from the Zwicky Transient Facility, we show that the new model
reliably infers rotational periods from sparsely sampled light curves, and
generally provides well-constrained posterior probability densities for the
model parameters. We propose this framework as an intermediate method between
fast, but very limited period detection algorithms and much more comprehensive,
but computationally expensive shape modeling based on ray-tracing codes.
