日期精度：到日 5 篇 · 到月 0 篇 · 到年 0 篇
Arnason–Schwarz 模型的闭式似然
We provide a general framework for the computationally efficient analysis, both Bayesian and classical, of integrated multi‐site recovery/recapture models in the presence of individual‐level covariates by extending the basic Arnason–Schwarz models and deriving closed‐form likelihood expressions, together with corresponding sufficient statistics.
通过模拟退火进行经典模型选择
Summary The classical approach to statistical analysis is usually based upon finding values for model parameters that maximize the likelihood function. Model choice in this context is often also based on the likelihood function, but with the addition of a penalty term for the number of parameters. Though models may be compared pairwise by using likelihood ratio tests for example, various criteria such as the Akaike information criterion have been proposed as alternatives…
多层捕获—再捕获数据的贝叶斯模型判别
Extending the work of Dupuis (1995), we motivate a range of biologically plausible models for multiple‐site capture‐recapture and show how the original Gibbs sampling algorithm of Dupuis can be extended to obtain posterior model probabilities using reversible jump Markov chain Monte Carlo. This model selection procedure improves upon previous analyses in two distinct ways. First, Bayesian model averaging provides a robust parameter estimation technique which properly…
总体规模的贝叶斯分析
We consider the problem of estimating the total size of a population from a series of incomplete census data. We observe that inference is typically highly sensitive to the choice of model and we demonstrate how Bayesian model averaging techniques easily overcome this problem. We combine and extend the work of Madigan & York (1997) and Dellaportas & Forster (1999) using reversible jump Markov chain Monte Carlo simulation to calculate posterior model probabilities which can…
一般列联表分析中对数线性模型的先验诱导
Log-linear modelling plays an important role in many statistical applications, particularly in the analysis of contingency table data.With the advent of powerful new computational techniques such as reversible jump MCMC, Bayesian analyses of these models, and in particular model selection and averaging, have become feasible. Coupled with this is the desire to construct and use suitably flexible prior structures which allow efficient computation while facilitating prior…