On the use of marginal posteriors in marginal likelihood estimation via importance sampling

نویسندگان
چکیده

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Marginal likelihood estimation via power posteriors

Model choice plays an increasingly important role in Statistics. From a Bayesian perspective a crucial goal is to compute the marginal likelihood of the data for a given model. This however is typically a difficult task since it amounts to integrating over all model parameters. The aim of this paper is to illustrate how this may be achieved using ideas from thermodynamic integration or path sam...

متن کامل

Accept–reject Metropolis–Hastings sampling and marginal likelihood estimation

We describe a method for estimating the marginal likelihood, based on CHIB (1995) and CHIB and JELIAZKOV (2001), when simulation from the posterior distribution of the model parameters is by the accept– reject Metropolis–Hastings (ARMH) algorithm. The method is developed for one-block and multiple-block ARMH algorithms and does not require the (typically) unknown normalizing constant of the pro...

متن کامل

On the Use of Marginal Likelihood in Model Selection

SUMMARY Based on the marginal likelihood approach, we develop a model selection criterion, MIC, for regression models with the general variance structure. These include weighted regression models, regression models with ARMA errors, growth curve models, and spatial correlation models. We show that MIC is a consistent criterion. For regression models with either constant or non-constant variance...

متن کامل

Marginal set likelihood for semiparametric copula estimation

Quantitative studies in many fields involve the analysis of multivariate data of diverse types, including measurements that we may consider binary, ordinal and continuous. One approach to the analysis of such mixed data is to use a copula model, in which the associations among the variables are parameterized separately from their univariate marginal distributions. The purpose of this article is...

متن کامل

Stepwise Signal Extraction via Marginal Likelihood.

This paper studies the estimation of stepwise signal. To determine the number and locations of change-points of the stepwise signal, we formulate a maximum marginal likelihood estimator, which can be computed with a quadratic cost using dynamic programming. We carry out extensive investigation on the choice of the prior distribution and study the asymptotic properties of the maximum marginal li...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Computational Statistics & Data Analysis

سال: 2014

ISSN: 0167-9473

DOI: 10.1016/j.csda.2014.03.004