[Faculty] Fwd: [CSRC.COLLOQUIUM] "Multi-Layered Convolutional Gaussian Process Models for Animal Movement"

[Jose Castillo]

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DATE:  *Friday, March 6, 2020*



TITLE:

*Multi-Layered Convolutional Gaussian Process Models for Animal Movement*


TIME:  *3:00-4:00PM*




LOCATION:
*GMCS 314*




SPEAKER/BIO:


*Dr. Henry Scharf, Assistant Professor, Department of Mathematics and
Statistics, San Diego State University*





ABSTRACT:

Gaussian processes are a fundamental statistical tool used in a wide range
of statistical applications. In the spatio-temporal setting, the covariance
of a Gaussian process is typically a function of relative locations in
space and time and must satisfy the condition of positive definiteness.
Several families of covariance functions exist that attempt to accommodate
the wide variety of dependence structures arising in different
applications, while ensuring positive definiteness. These parametric
families can be restrictive and are insufficient in some situations. In
contrast, process convolutions represent a flexible, interpretable approach
to defining the covariance of a Gaussian process with modest requirements
to ensure validity. We introduce a generalization to the process
convolution approach that employs multiple convolutions in sequence to form
what we term a "process convolution chain." In our proposed multi-layered
framework, complex dependencies that arise from a combination of different
interacting mechanisms are decomposed into a series of interpretable kernel
smoothers. We demonstrate an application of process convolution chains to
the study of the movement of killer whales, in which the paths taken by
multiple individuals are not independent, but reflect dynamic social
interactions within the population. We propose a process convolution chain
for dependent movement that allows us to make inference about the dynamic
social structure in the study population, and to leverage the positive
dependence among paths to reduce the uncertainty surrounding the location
of an individual during gaps between observations.



Host: Jose Castillo

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