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Bayesian analysis of mark‐recapture data with travel time‐dependent survival probabilities
Author(s) -
Muthukumarana Saman,
Schwarz Carl J.,
Swartz Tim B.
Publication year - 2008
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.1002/cjs.5550360103
Subject(s) - markov chain monte carlo , bayesian probability , mark and recapture , likelihood function , statistics , econometrics , computer science , markov chain , maximum likelihood , mathematics , demography , population , sociology
The authors extend the classical Cormack‐Jolly‐Seber mark‐recapture model to account for both temporal and spatial movement through a series of markers (e.g., dams). Survival rates are modeled as a function of (possibly) unobserved travel times. Because of the complex nature of the likelihood, they use a Bayesian approach based on the complete data likelihood, and integrate the posterior through Markov chain Monte Carlo methods. They test the model through simulations and apply it also to actual salmon data arising from the Columbia river system. The methodology was developed for use by the Pacific Ocean Shelf Tracking (POST) project.