Implementation of reversible jump MCMC algorithm to segment the piecewise Polynomial Regression
نویسندگان
چکیده
منابع مشابه
Reversible jump MCMC
Statistical problems where ‘the number of things you don’t know is one of the things you don’t know’ are ubiquitous in statistical modelling. They arise both in traditional modelling situations such as variable selection in regression, and in more novel methodologies such as object recognition, signal processing, and Bayesian nonparametrics. All such ‘trans-dimensional’ problems can be formulat...
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In this paper we introduce the problem of assessing convergence of reversible jump MCMC algorithms on the basis of simulation output. We discuss the various direct approaches which could be employed, together with their associated drawbacks. Using the example of fitting a graphical Gaussian model via RJMCMC, we show how the simulation output for models which can be parameterised so that paramet...
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Variable selection is a topic of great importance in high-dimensional statistical modeling and has a wide range of real-world applications. Many variable selection techniques have been proposed in the context of linear regression, and the Lasso model is probably one of the most popular penalized regression techniques. In this paper, we propose a new, fully hierarchical, Bayesian version of the ...
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ژورنال
عنوان ژورنال: International Journal of Advances in Intelligent Informatics
سال: 2016
ISSN: 2548-3161,2442-6571
DOI: 10.26555/ijain.v2i2.62