Bayesian capture-recapture methods for error detection and estimation of population size: Heterogeneity and dependence

By Sanjib Basu, N. Ebrahimi. 2001

71 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/2620aa3c47da68118ae8eba0694ddad44ae64fa7

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In Artificial Intelligence

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Citations in the corpus

Estimating Size and Composition of Biological Communities by Modeling the Occurrence of Species (2005, background): “…in detection among species (Agresti 1994; Norris and Pollock 1996; Boulinier et al. 1998; Coull and Agresti 1999; Fienberg et al. 1999; Pledger 2000; Basu and Ebrahimi 2001; Tardella 2002; Dorazio and Royle 2003) implicitly assumes that every species is present at every sample location.”
Estimation of binomial parameters when both n, p are unknown (2005, background): “The software reliability literature also includes a substantial body of work that relates to the binomial n problem when p is unknown; see Basu(2003), Basu and Ebrahimi(2001) for many references and Bayesian approaches, and Zacks et.al.”
Objective Priors for Discrete Parameter Spaces (2012, background): “Basu and Ebrahimi (2001) developed objective priors for a related problem in software reliability.”
Analysis of Multinomial Models With Unknown Index Using Data Augmentation (2007, methodology): “Although Bayesian analyses of this model have been considered previously (Fienberg, Johnson, and Junker 1999; Basu and Ebrahimi 2001; Tardella 2002), the development of Gibbs samplers for models that condition onN has proved to be challenging because the dimension of the nuisance parameter p = (p1, ”
Bayesian Estimation of Discrete Multivariate Latent Structure Models With Structural Zeros (2014, methodology): “A special case of this setup are Bayesian formulations of multiple-recapture population-size estimation using latent variables (see, e.g., Fienberg, Johnson, and Junker 1999; Basu and Ebrahimi 2001; Manrique-Vallier and Fienberg 2008), in which the objective is to estimate the size of the unobserved”