On marginal likelihood computation in change-point models
نویسندگان
چکیده
منابع مشابه
On marginal likelihood computation in change-point models
Change-point models are useful for modeling times series subject to structural breaks. For interpretation and forecasting, it is essential to estimate correctly the number of change points in this class of models. In Bayesian inference, the number of changepoints is typically chosen by the marginal likelihood criterion, computed by Chib’s method. This method requires to select a value in the pa...
متن کاملCore Discussion Paper 2009/61 on Marginal Likelihood Computation in Change-point Models
Change-point models are useful for modeling time series subject to structural breaks. For interpretation and forecasting, it is essential to estimate correctly the number of change points in this class of models. In Bayesian inference, the number of change points is typically chosen by the marginal likelihood criterion, computed by Chib’s method. This method requires to select a value in the pa...
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In a Bayesian analysis, different models can be compared on the basis of the expected or marginal likelihood they attain. Many methods have been devised to compute the marginal likelihood, but simplicity is not the strongest point of most methods. At the same time, the precision of methods is often questionable. In this paper several methods are presented in a common framework. The explanation ...
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In Chib (1995), a method for approximating marginal densities in a Bayesian setting is proposed, with one proeminent application being the estimation of the number of components in a normal mixture. As pointed out in Neal (1999) and Frühwirth-Schnatter (2004), the approximation often fails short of providing a proper approximation to the true marginal densities because of the well-known label s...
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For a vector y ∈ Rn and a model subspace X ⊂ Rn, the residual configuration statistic is what remains of y when translations in the model space and scalar multiplication are ignored. The configuration statistic for a linear Gaussian model has a distribution that depends only on variance-component ratios, or similar ratio parameters in the covariance function of a spatial model. The marginal lik...
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ژورنال
عنوان ژورنال: Computational Statistics & Data Analysis
سال: 2012
ISSN: 0167-9473
DOI: 10.1016/j.csda.2010.06.025