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- 저자 : L. Mark Berliner (검색결과 2 건)
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Approximated sensitivity analysis in posterior predictive distribution
김용구,L. Mark Berliner,김달호 한국통계학회 2015 Journal of the Korean Statistical Society Vol.44 No.2
In Bayesian statistics, a model can be assessed by checking that the model fits the data, which is addressed by using the posterior predictive distribution for a discrepancy, an extension of classical test statistics to allow dependence on unknown (nuisance) parameters. Posterior predictive assessment of model fitness allows more direct assessment of the discrepancy between data and the posited model. The sensitivity analysis revealed that the effect of priors on parameter inferences is different from their effect on marginal density and predictive posterior distribution. In this paper, we explore the effect of the prior (or posterior) distribution on the corresponding posterior predictive distribution. The approximate sensitivity of the posterior predictive distribution is studied in terms of information measure including the Kullback–Leibler divergence. As an illustration, we applied these results to the simple spatial model settings.
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Bayesian diffusion process models with time-varying parameters
김용구,강석복,L. Mark Berliner 한국통계학회 2012 Journal of the Korean Statistical Society Vol.41 No.1
The diffusion process is a widely used statistical model for many natural dynamic phenomena but its inference is very complicated because complete data describing the diffusion sample path is not necessarily available. In addition, data is often collected with substantial uncertainty and it is not uncommon to have missing observations. Thus, the observed process will be discrete over a finite time period and the marginal likelihood given by this discrete data is not always available. In this paper, we consider a class of nonstationary diffusion process models with not only the measurement error but also discretely timevarying parameters which are modeled via a state space model. Hierarchical Bayesian inference for such a diffusion process model with time-varying parameters is applied to financial data.
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