5,353 research outputs found
Modeling and estimation of multi-source clustering in crime and security data
While the presence of clustering in crime and security event data is well
established, the mechanism(s) by which clustering arises is not fully
understood. Both contagion models and history independent correlation models
are applied, but not simultaneously. In an attempt to disentangle contagion
from other types of correlation, we consider a Hawkes process with background
rate driven by a log Gaussian Cox process. Our inference methodology is an
efficient Metropolis adjusted Langevin algorithm for filtering of the intensity
and estimation of the model parameters. We apply the methodology to property
and violent crime data from Chicago, terrorist attack data from Northern
Ireland and Israel, and civilian casualty data from Iraq. For each data set we
quantify the uncertainty in the levels of contagion vs. history independent
correlation.Comment: Published in at http://dx.doi.org/10.1214/13-AOAS647 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Latent Self-Exciting Point Process Model for Spatial-Temporal Networks
We propose a latent self-exciting point process model that describes
geographically distributed interactions between pairs of entities. In contrast
to most existing approaches that assume fully observable interactions, here we
consider a scenario where certain interaction events lack information about
participants. Instead, this information needs to be inferred from the available
observations. We develop an efficient approximate algorithm based on
variational expectation-maximization to infer unknown participants in an event
given the location and the time of the event. We validate the model on
synthetic as well as real-world data, and obtain very promising results on the
identity-inference task. We also use our model to predict the timing and
participants of future events, and demonstrate that it compares favorably with
baseline approaches.Comment: 20 pages, 6 figures (v3); 11 pages, 6 figures (v2); previous version
appeared in the 9th Bayesian Modeling Applications Workshop, UAI'1
Multivariate Spatiotemporal Hawkes Processes and Network Reconstruction
There is often latent network structure in spatial and temporal data and the
tools of network analysis can yield fascinating insights into such data. In
this paper, we develop a nonparametric method for network reconstruction from
spatiotemporal data sets using multivariate Hawkes processes. In contrast to
prior work on network reconstruction with point-process models, which has often
focused on exclusively temporal information, our approach uses both temporal
and spatial information and does not assume a specific parametric form of
network dynamics. This leads to an effective way of recovering an underlying
network. We illustrate our approach using both synthetic networks and networks
constructed from real-world data sets (a location-based social media network, a
narrative of crime events, and violent gang crimes). Our results demonstrate
that, in comparison to using only temporal data, our spatiotemporal approach
yields improved network reconstruction, providing a basis for meaningful
subsequent analysis --- such as community structure and motif analysis --- of
the reconstructed networks
Multivariate Hawkes Processes for Large-scale Inference
In this paper, we present a framework for fitting multivariate Hawkes
processes for large-scale problems both in the number of events in the observed
history and the number of event types (i.e. dimensions). The proposed
Low-Rank Hawkes Process (LRHP) framework introduces a low-rank approximation of
the kernel matrix that allows to perform the nonparametric learning of the
triggering kernels using at most operations, where is the
rank of the approximation (). This comes as a major improvement to
the existing state-of-the-art inference algorithms that are in .
Furthermore, the low-rank approximation allows LRHP to learn representative
patterns of interaction between event types, which may be valuable for the
analysis of such complex processes in real world datasets. The efficiency and
scalability of our approach is illustrated with numerical experiments on
simulated as well as real datasets.Comment: 16 pages, 5 figure
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