  The detection and analysis of events within massive collections of
time-series has become an extremely important task for time-domain astronomy.
In particular, many scientific investigations (e.g. the analysis of
microlensing and other transients) begin with the detection of isolated events
in irregularly-sampled series with both non-linear trends and non-Gaussian
noise. We outline a semi-parametric, robust, parallel method for identifying
variability and isolated events at multiple scales in the presence of the above
complications. This approach harnesses the power of Bayesian modeling while
maintaining much of the speed and scalability of more ad-hoc machine learning
approaches. We also contrast this work with event detection methods from other
fields, highlighting the unique challenges posed by astronomical surveys.
Finally, we present results from the application of this method to 87.2 million
EROS-2 sources, where we have obtained a greater than 100-fold reduction in
candidates for certain types of phenomena while creating high-quality features
for subsequent analyses.
