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Probabilistic Querying of Continuous-Time Sequential Events
Alex Boyd · Yuxin Chang · Stephan Mandt · Padhraic Smyth

In this work, we focus on the problem of answering probabilistic queries in the context of continuous-time event data, focusing in particular on neural autoregressive models. Probabilistic queries have potentially broad applications and can include queries such as “the probability of an event of type A occurring before type B” or “the probability of at least one event of type A occurring before time T”. Given that computation of such query probabilities is usually intractable and lacks analytical forms in general, we propose a general theoretical framework and a novel marginalization scheme that enables us to leverage importance sampling to answer these queries in a computationally efficient manner for any black-box autoregressive model. We evaluate our approach by presenting results with multiple real-world continuous-time event datasets, and demonstrate that our approach can be significantly more computationally efficient than the naive estimation. Please refer to the PDF for the one-page extended abstract for additional details.

Author Information

Alex Boyd (UC Irvine)
Yuxin Chang (UC Irvine)
Stephan Mandt (University of California, Irvine)
Padhraic Smyth (University of California, Irvine)

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