Implementation of Gibbs Sampling within Bayesian Inference and its Applications in Actuarial Science | Zendy

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Implementation of Gibbs Sampling within Bayesian Inference and its Applications in Actuarial Science

Author(s) -

Colton Gearhart

Publication year - 2018

Publication title -

siam undergraduate research online

Language(s) - English

Resource type - Journals

ISSN - 2327-7807

DOI - 10.1137/17s016609

Subject(s) - gibbs sampling , inference , bayesian probability , bayesian inference , econometrics , sampling (signal processing) , computer science , actuarial science , economics , artificial intelligence , filter (signal processing) , computer vision

This paper discusses how a variety of actuarial models can be implemented and analyzed with a Bayesian approach using Gibbs sampling, a Markov chain Monte Carlo method. This approach allows a practitioner to analyze complicated actuarial models by reducing them to simpler, more manageable models. Furthermore, general properties of Gibbs sampling are discussed through a simulation approach. AMS 2010 subject classification: Primary 62F40; Secondary 62E17

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