Bayesian model averaging: A systematic review and conceptual classification
Bayesian model averaging (BMA) provides a coherent and systematic mechanism for
accounting for model uncertainty. It can be regarded as an direct application of Bayesian …
accounting for model uncertainty. It can be regarded as an direct application of Bayesian …
Bayesian inference for categorical data analysis
This article surveys Bayesian methods for categorical data analysis, with primary emphasis
on contingency table analysis. Early innovations were proposed by Good (1953, 1956 …
on contingency table analysis. Early innovations were proposed by Good (1953, 1956 …
Analysis of multinomial models with unknown index using data augmentation
Multinomial models with unknown index (“sample size”) arise in many practical settings. In
practice, Bayesian analysis of such models has proved difficult because the dimension of …
practice, Bayesian analysis of such models has proved difficult because the dimension of …
Efficient construction of reversible jump Markov chain Monte Carlo proposal distributions
The major implementational problem for reversible jump Markov chain Monte Carlo methods
is that there is commonly no natural way to choose jump proposals since there is no …
is that there is commonly no natural way to choose jump proposals since there is no …
Parameter-expanded data augmentation for Bayesian analysis of capture–recapture models
Data augmentation (DA) is a flexible tool for analyzing closed and open population models
of capture–recapture data, especially models which include sources of hetereogeneity …
of capture–recapture data, especially models which include sources of hetereogeneity …
A need for speed in Bayesian population models: a practical guide to marginalizing and recovering discrete latent states
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 …
discrete latent states. Here, we discuss an alternative approach to discrete latent states …
[كتاب][B] Introduction to hierarchical Bayesian modeling for ecological data
5.1 Data to estimate spawning run in The Scorff River... 109 5.2 Informative Beta pdfs for (θ,
α, β, τ, δ, π)........ 113 5.3 Marginal posterior statistics based on year 1995.... 119 5.4 …
α, β, τ, δ, π)........ 113 5.3 Marginal posterior statistics based on year 1995.... 119 5.4 …
Bayesian population size estimation using Dirichlet process mixtures
We introduce a new Bayesian nonparametric method for estimating the size of a closed
population from multiple-recapture data. Our method, based on Dirichlet process mixtures …
population from multiple-recapture data. Our method, based on Dirichlet process mixtures …
Multiple systems estimation (or capture-recapture estimation) to inform public policy
Applications of estimating population sizes range from estimating human or ecological
population size within regions or countries to estimating the hidden number of civilian …
population size within regions or countries to estimating the hidden number of civilian …
Multiple-systems analysis for the quantification of modern slavery: classical and Bayesian approaches
Multiple-systems estimation is a key approach for quantifying hidden populations such as
the number of victims of modern slavery. The UK Government published an estimate of …
the number of victims of modern slavery. The UK Government published an estimate of …