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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.14073 |
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| _version_ | 1866913884044001280 |
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| author | Teimouri, Mahdi |
| author_facet | Teimouri, Mahdi |
| contents | In general, the statistical simulation approaches are referred to as the Monte Carlo methods as a whole. The broad class of the Monte Carlo methods involves the Markov chain Monte Carlo (MCMC) techniques that attract the attention of researchers from a wide variety of study fields. The main focus of this report is to provide a framework for all users who are interested in implementing the MCMC approaches in their investigations, especially the Gibbs sampling. I have tried, if possible, to eliminate the proofs, but reader is expected to know some topics in elementary calculus (including mathematical function, limit, derivative, partial derivative, simple integral) and statistics (including random variables, expected value and variance, moment generating function, multivariate distribution, distribution of a functions of random variable, and the central limit theorem). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_14073 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Digesting Gibbs Sampling Using R Teimouri, Mahdi Computation In general, the statistical simulation approaches are referred to as the Monte Carlo methods as a whole. The broad class of the Monte Carlo methods involves the Markov chain Monte Carlo (MCMC) techniques that attract the attention of researchers from a wide variety of study fields. The main focus of this report is to provide a framework for all users who are interested in implementing the MCMC approaches in their investigations, especially the Gibbs sampling. I have tried, if possible, to eliminate the proofs, but reader is expected to know some topics in elementary calculus (including mathematical function, limit, derivative, partial derivative, simple integral) and statistics (including random variables, expected value and variance, moment generating function, multivariate distribution, distribution of a functions of random variable, and the central limit theorem). |
| title | Digesting Gibbs Sampling Using R |
| topic | Computation |
| url | https://arxiv.org/abs/2410.14073 |