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dc.contributor.advisorSobel, Marc J.
dc.creatorAckerman, Samuel
dc.date.accessioned2020-11-03T15:34:03Z
dc.date.available2020-11-03T15:34:03Z
dc.date.issued2018
dc.identifier.urihttp://hdl.handle.net/20.500.12613/2527
dc.description.abstractOur data consist of measurements of 22 sharks' movements within a 366-acre tidal basin. The measurements are made at irregular time points over a 16-month interval. Constant-length observation intervals would have been desirable, but are often infeasible in practice. We model the sharks' paths at short constant-length intervals by inferring their behavior (feeding vs transiting), interpolating their locations, and estimating parameters of motion (speed and turning angle) in environmental and ecological contexts. We are interested in inferring regional differences in the sharks' behavior, and behavioral interaction between them. Our method uses particle filters, a computational Bayesian technique designed to sequentially model a dynamic system. We discuss how resampling is used to approximate arbitrary densities, and illustrate its use in a simple example of a particle filter implementation of a state-space model. We then introduce a particular model formulation that uses conditioning to introduce unobserved parameters for the shark's behaviors. We show how the irregularly-observed shark locations can be modeled by interpolation as a set of movements at constant-length time intervals. We use a spline method for generating approximations of the ground truth at these intervals for comparison with our model. Finally, we demonstrate our model's estimates of the sharks' behavioral and ecological parameters of interest on a subset of the observed data.
dc.format.extent228 pages
dc.language.isoeng
dc.publisherTemple University. Libraries
dc.relation.ispartofTheses and Dissertations
dc.rightsIN COPYRIGHT- This Rights Statement can be used for an Item that is in copyright. Using this statement implies that the organization making this Item available has determined that the Item is in copyright and either is the rights-holder, has obtained permission from the rights-holder(s) to make their Work(s) available, or makes the Item available under an exception or limitation to copyright (including Fair Use) that entitles it to make the Item available.
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subjectStatistics
dc.subjectBiology
dc.subjectComputer Science
dc.subjectAnimal Telemetry
dc.subjectBayesian Statistics
dc.subjectParticle Filter
dc.subjectState Space Model
dc.titleA Probabilistic Characterization of Shark Movement Using Location Tracking Data
dc.typeText
dc.type.genreThesis/Dissertation
dc.contributor.committeememberHeiberger, Richard M., 1945-
dc.contributor.committeememberCarides, Alexandra
dc.contributor.committeememberO'Connor, Michael
dc.contributor.committeememberSouvenir, Richard M.
dc.description.departmentStatistics
dc.relation.doihttp://dx.doi.org/10.34944/dspace/2509
dc.ada.noteFor Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact scholarshare@temple.edu
dc.description.degreePh.D.
refterms.dateFOA2020-11-03T15:34:03Z


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