東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Linked to FindBook

Google Book

Amazon

博客來

Bayesian Hierarchical Model of Subsampled Multiply Labeled Dolphin Movement Data.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Bayesian Hierarchical Model of Subsampled Multiply Labeled Dolphin Movement Data./
Author:	Nezamabadi, Navid.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2022,
Description:	54 p.
Notes:	Source: Masters Abstracts International, Volume: 84-01.
Contained By:	Masters Abstracts International84-01.
Subject:	Statistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29258128
ISBN:	9798834070962

Bayesian Hierarchical Model of Subsampled Multiply Labeled Dolphin Movement Data.
Nezamabadi, Navid.

Bayesian Hierarchical Model of Subsampled Multiply Labeled Dolphin Movement Data. - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 54 p.

Source: Masters Abstracts International, Volume: 84-01.

Thesis (M.S.)--San Diego State University, 2022.

This item must not be sold to any third party vendors.

In recent years there has been an uptick in the volume of research addressing the social behaviors of animals, specifically non-herd movement patterns. There have been many notable approaches, mostly centered around the modeling of interconnected networks of animals. Developing such models has been favored as the social relations of animals in a group cannot be easily disregarded when attempting to analyze their movement patterns. Subsequently, creation of hierarchical Bayesian approaches has emerged, which seek to model the social relations and their influence on movement patterns. A primary source of information about social behavior has been movement data which has become significantly more accessible in recent years as aerial drone footage has become more omnipresent. This thesis contends with the social movement patterns of Long-Beaked Common dolphins. An unfortunate drawback of using aerial movement data with dolphins is the matter of labeling. As individuals are quick to move around, they often move outside a drone's field of view and are subsequently relabeled with a new label upon reentry. This leads to concerns over multiply labeled movement data, which recent works have been addressing, and which is rather computationally taxing. The computational challenges of prior works was the impetus of the current work, which seeks to use subsampling of small groups of individuals to infer results for underlying behavioral parameters for an entire population. An augmented simple random sample was used to obtain six different sample sizes ranging from as few as three concurrently tracked individuals to as many as eight. Bayesian hierarchical models were fit to the data for each of these different sample sizes, and posterior distributions were compared. The primary objective was to see how small of a subsample would yield meaningful posterior distributions. There were on average 40individuals present throughout the observation period. It was found that a subsample of six individuals was sufficient to obtain adequate posteriors considered representative of the entire network of individuals present. Thus, this sampling approach has further developed the possibility of modeling larger groups of dolphins than previously attempted.

ISBN: 9798834070962Subjects--Topical Terms:

517247
Statistics.
Subjects--Index Terms:

Subsampled multiply labeled dolphin movement

Bayesian Hierarchical Model of Subsampled Multiply Labeled Dolphin Movement Data.
LDR:03302nmm a2200325 4500 001 2348320
005 20220908123030.5
008 241004s2022 ||||||||||||||||| ||eng d
020 $a 9798834070962
035 $a (MiAaPQ)AAI29258128
035 $a AAI29258128
040 $a MiAaPQ $c MiAaPQ
100 1 $a Nezamabadi, Navid. $3 3687655
245 1 0 $a Bayesian Hierarchical Model of Subsampled Multiply Labeled Dolphin Movement Data.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2022
300 $a 54 p.
500 $a Source: Masters Abstracts International, Volume: 84-01.
500 $a Advisor: Scharf, Henry.
502 $a Thesis (M.S.)--San Diego State University, 2022.
506 $a This item must not be sold to any third party vendors.
520 $a In recent years there has been an uptick in the volume of research addressing the social behaviors of animals, specifically non-herd movement patterns. There have been many notable approaches, mostly centered around the modeling of interconnected networks of animals. Developing such models has been favored as the social relations of animals in a group cannot be easily disregarded when attempting to analyze their movement patterns. Subsequently, creation of hierarchical Bayesian approaches has emerged, which seek to model the social relations and their influence on movement patterns. A primary source of information about social behavior has been movement data which has become significantly more accessible in recent years as aerial drone footage has become more omnipresent. This thesis contends with the social movement patterns of Long-Beaked Common dolphins. An unfortunate drawback of using aerial movement data with dolphins is the matter of labeling. As individuals are quick to move around, they often move outside a drone's field of view and are subsequently relabeled with a new label upon reentry. This leads to concerns over multiply labeled movement data, which recent works have been addressing, and which is rather computationally taxing. The computational challenges of prior works was the impetus of the current work, which seeks to use subsampling of small groups of individuals to infer results for underlying behavioral parameters for an entire population. An augmented simple random sample was used to obtain six different sample sizes ranging from as few as three concurrently tracked individuals to as many as eight. Bayesian hierarchical models were fit to the data for each of these different sample sizes, and posterior distributions were compared. The primary objective was to see how small of a subsample would yield meaningful posterior distributions. There were on average 40individuals present throughout the observation period. It was found that a subsample of six individuals was sufficient to obtain adequate posteriors considered representative of the entire network of individuals present. Thus, this sampling approach has further developed the possibility of modeling larger groups of dolphins than previously attempted.
590 $a School code: 0220.
650 4 $a Statistics. $3 517247
650 4 $a Aquatic sciences. $3 3174300
650 4 $a Behavioral sciences. $3 529833
653 $a Subsampled multiply labeled dolphin movement
690 $a 0463
690 $a 0602
690 $a 0792
710 2 $a San Diego State University. $b Statistics. $3 3173088
773 0 $t Masters Abstracts International $g 84-01.
790 $a 0220
791 $a M.S.
792 $a 2022
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29258128