The marginal likelihood and model selection
Model selection via marginal likelihood estimation by combining thermodynamic integration and gradient matching
... a model similar to those investi- gated in our paper, the log likelihood landscapes for the exact method and gradient matching are very different, despite the fact that the maximum likelihood ...
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On Marginal Likelihood Computation in Change-point Models
... 2) Concerning the model selection criteria (Table 15), the performance is not good. The BIC selects the correct number of breaks only in 24 per cent of the repetitions and puts too much weight (50 per cent) ...
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On marginal likelihood computation in change-point models
... 2) Concerning the model selection criteria (Table 15), the performance is not good. The BIC selects the correct number of breaks only in 24 per cent of the repetitions and puts too much weight (50 per cent) ...
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Marginal likelihood for Markov-switching and change-point GARCH models
... the marginal likelihood based model selection varies when using three different priors for the GARCH ...the marginal likelihood if an additional regime is ...
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Marginal Likelihood for Markov-Switching and Change-Point Garch Models
... the marginal likelihood based model selection varies when using three different priors for the GARCH ...the marginal likelihood if an additional regime is ...
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The marginal likelihood of Structural Time Series Models, with application to the euroareaa nd US NAIRU
... particular model, and in some cases discriminating between different specifications can be a difficult ...the marginal likeli- hood, the Bayesian framework offer a conceptually simple answer to the ...
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Marginal Conceptual Predictive Statistic for Mixed Model Selection
... a model selection criterion, it means that as the sample size increases, the model selection will select the true model with probability ...other model selection criteria. ...
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Likelihood theory, prediction, model selection: asymptotic connections.
... profile likelihood for inference in the presence of nuisance ...is model selection, where information criteria based on penalisation of maximised likelihood have been proposed starting from ...
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Model selection confidence sets by likelihood ratio testing
... Model Selection Confidence Sets by Likelihood Ratio Testing Chao Zheng 1 , Davide Ferrari 2 and Yuhong Yang 3 2 Lancaster University, 2 University of Melbourne and 3 University of Minnesota ...
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On the use of marginal posteriors in marginal likelihood estimation via importance sampling.
... univariate marginal posterior densities as the only remaining source of ...estimating marginal probabilities, the approach proposed here is particularly suited for Gibbs sampling settings where ...
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Marginal Likelihood Estimation with the Cross Entropy Method
... More specifically, not only do existing approaches often require nontrivial programming efforts, most involve using MCMC draws to compute certain Monte Carlo averages, which are then used to derive an estimate of the ...
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Marginal Likelihood Integrals for Mixtures of Independence Models
... of marginal likelihood integrals is central to Bayesian ...examine marginal likelihood integrals for a class of mixture models for discrete ...
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Profit-Optimal Model and Target Size Selection with Variable Marginal Costs
... random selection of prospects is uncertain, many of these corporations use data mining techniques to characterize good prospects in their target audiences and improve the likelihood of ...to model ...
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Marginal likelihood calculation for gelfand dey and Chib Method
... Gelfand-Dey (GD) is a general method. The GD method is efficient and utilizes the same routines when calculating the ML for different models. Meanwhile, the Chib method is often thought of as a more accurate method of ...
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An Asymptotic Behaviour of the Marginal Likelihood for General Markov Models
... the model so that the induced parameterization becomes ...the model structure which is described by Zwiernik and Smith (2011b) and Zwiernik and Smith ...
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On Marginal Quasi-Likelihood Inference in Generalized Linear Mixed Models
... Further, note that in the present approach it is not only that the MQL method uses the almost exact means of the responses but it also completely avoids the use of the working covariance, yielding consistent as well as ...
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Marginal Maximum Likelihood Estimation of Item Response Models in R
... Baruch College, The City University of New York Abstract Item response theory (IRT) models are a class of statistical models used by researchers to describe the response behaviors of individuals to a set of categorically ...
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Marginal Likelihood for Markov-Switching and Change-Point GARCH Models
... MS- and CP-GARCH models are flexible alternatives to GARCH models with fixed parame- ters. We estimate them by Bayesian inference using data augmentation because of the path dependence problem. We choose the number of ...
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From Language to Programs: Bridging Reinforcement Learning and Maximum Marginal Likelihood
... [email protected] Abstract Our goal is to learn a semantic parser that maps natural language utterances into ex- ecutable programs when only indirect su- pervision is available: examples are la- beled with the ...
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The Composite Marginal Likelihood (CML) Estimation of Panel Ordered-Response Models
... Overall, the results suggest that the CML method is able to recover the true parameters in all the cases considered in the paper, irrespective of the type of covariance matrix (diagonal versus non-diagonal) of the random ...
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