Artikel: A need for speed in Bayesian population models: a practical guide to marginalizing and recovering discrete latent states.
Ecological applications : a publication of the Ecological Society of America
2020 Band 30, Heft 5, Seite(n) e02112
Abstract: Bayesian population models can be exceedingly slow due, in part, to the choice to simulate discrete latent states. Here, we discuss an alternative approach to discrete latent states, marginalization, that forms the basis of maximum likelihood population ... ...
Abstract | Bayesian population models can be exceedingly slow due, in part, to the choice to simulate discrete latent states. Here, we discuss an alternative approach to discrete latent states, marginalization, that forms the basis of maximum likelihood population models and is much faster. Our manuscript has two goals: (1) to introduce readers unfamiliar with marginalization to the concept and provide worked examples and (2) to address topics associated with marginalization that have not been previously synthesized and are relevant to both Bayesian and maximum likelihood models. We begin by explaining marginalization using a Cormack-Jolly-Seber model. Next, we apply marginalization to multistate capture-recapture, community occupancy, and integrated population models and briefly discuss random effects, priors, and pseudo-R |
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Mesh-Begriff(e) | Bayes Theorem ; Computer Simulation ; Likelihood Functions ; Models, Statistical ; Population Density ; Population Dynamics |
Sprache | Englisch |
Erscheinungsdatum | 2020-04-01 |
Erscheinungsland | United States |
Dokumenttyp | Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S. |
ZDB-ID | 1074505-1 |
ISSN | 1939-5582 ; 1051-0761 |
ISSN (online) | 1939-5582 |
ISSN | 1051-0761 |
DOI | 10.1002/eap.2112 |
Datenquelle | MEDical Literature Analysis and Retrieval System OnLINE |
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