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Title: A Stein-Papangelou Goodness-of-Fit Test for Point Processes.
Point processes provide a powerful framework for modeling the distribution and interactions of events in time or space. Their flexibility has given rise to a variety of sophisticated models in statistics and machine learning, yet model diagnostic and criticism techniques re- main underdeveloped. In this work, we pro- pose a general Stein operator for point pro- cesses based on the Papangelou conditional intensity function. We then establish a kernel goodness-of-fit test by defining a Stein dis- crepancy measure for general point processes. Notably, our test also applies to non-Poisson point processes whose intensity functions con- tain intractable normalization constants due to the presence of complex interactions among points. We apply our proposed test to sev- eral point process models, and show that it outperforms a two-sample test based on the maximum mean discrepancy.  more » « less
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Journal Name:
Artificial Intelligence and Statistics (AISTATS 2019)
Medium: X
Sponsoring Org:
National Science Foundation
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