## Individual based population inference using tagging data

Publication: Research › Report – Annual report year: 2010

### Standard

**Individual based population inference using tagging data.** / Pedersen, Martin Wæver; Thygesen, Uffe Høgsbro; Baktoft, Henrik; Madsen, Henrik.

Publication: Research › Report – Annual report year: 2010

### Harvard

*Individual based population inference using tagging data*. Technical University of Denmark, DTU Informatics, Building 321, Kgs. Lyngby. IMM-Technical Report-2010-11

### APA

*Individual based population inference using tagging data*. Kgs. Lyngby: Technical University of Denmark, DTU Informatics, Building 321. (IMM-Technical Report-2010-11).

### CBE

### MLA

*Individual based population inference using tagging data*Kgs. Lyngby: Technical University of Denmark, DTU Informatics, Building 321. 2010. (IMM-Technical Report-2010-11).

### Vancouver

### Author

### Bibtex

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### RIS

TY - RPRT

T1 - Individual based population inference using tagging data

AU - Pedersen,Martin Wæver

AU - Thygesen,Uffe Høgsbro

AU - Baktoft,Henrik

AU - Madsen,Henrik

PY - 2010

Y1 - 2010

N2 - A hierarchical framework for simultaneous analysis of multiple related individual datasets is presented. The approach is very similar to mixed effects modelling as known from statistical theory. The model used at the individual level is, in principle, irrelevant as long as a maximum likelihood estimate and its uncertainty (Hessian) can be computed. The individual model used in this text is a hidden Markov model. A simulation study concerning a two-dimensional biased random walk is examined to verify the consistency of the hierarchical estimation framework. In addition, a study based on acoustic telemetry data from pike illustrates how the framework can identify individuals that deviate from the remaining population.

AB - A hierarchical framework for simultaneous analysis of multiple related individual datasets is presented. The approach is very similar to mixed effects modelling as known from statistical theory. The model used at the individual level is, in principle, irrelevant as long as a maximum likelihood estimate and its uncertainty (Hessian) can be computed. The individual model used in this text is a hidden Markov model. A simulation study concerning a two-dimensional biased random walk is examined to verify the consistency of the hierarchical estimation framework. In addition, a study based on acoustic telemetry data from pike illustrates how the framework can identify individuals that deviate from the remaining population.

M3 - Report

BT - Individual based population inference using tagging data

PB - Technical University of Denmark, DTU Informatics, Building 321

ER -