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Final project of STAT 547 - probability of statistics at UBC

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Name		Name	Last commit message	Last commit date
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README		README
STAT_547___Final_project-2.pdf		STAT_547___Final_project-2.pdf

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Final project is available in pdf.

Notes from finding articles while researching:
Link to articles:
- Main article: [Is Gibbs sampling faster than Hamiltonian Monte Carlo on GLMs? - Luu et al. 2024](https://arxiv.org/abs/2410.03630)
- Link to code from article: https://github.com/UBC-Stat-ML/gibbs-vs-hmc-mev

Slice sampling methods:
- [qslice: Implementations of Various Slice Samplers (R package)](https://CRAN.R-project.org/package=qslice)
- [Slice sampling - Neal 2003](https://projecteuclid.org/journals/annals-of-statistics/volume-31/issue-3/Slice-sampling/10.1214/aos/1056562461.full)
  - Mentioned 
- [Nishihara et al. (2014)](https://jmlr.org/papers/v15/nishihara14a.html)
- [Murray et al. (2010)](https://proceedings.mlr.press/v9/murray10a)

Other links:
- [Non-reversible parallel tempering: A scalable highly parallel MCMC scheme - Syed et al. 2021](https://rss.onlinelibrary.wiley.com/doi/10.1111/rssb.12464)
  - Mentioned at the top here: https://pigeons.run/dev/distributed/
- [Parallel Tempering With a Variational Reference - Surjanovic et al. 2022](https://arxiv.org/abs/2206.00080)
  - Mentioned here: https://pigeons.run/dev/variational/
- [autoMALA: Locally adaptive Metropolis-adjusted Langevin algorithm - Biron-Lattes et al. 2024](https://proceedings.mlr.press/v238/biron-lattes24a.html)
  - Mentioned here: https://pigeons.run/dev/
- A simple sampler for the horseshoe distribution https://arxiv.org/pdf/1508.03884

- [nimbleAPT: Adaptive Parallel Tempering for 'NIMBLE' (R package)](https://CRAN.R-project.org/package=nimbleAPT)

- [Bayesian Data Analysis, Third edition - Gelman et al. 2021](http://www.stat.columbia.edu/~gelman/book/)
- [A Student's Guide to Bayesian Statistics - Lambert 2018](https://sites.math.rutgers.edu/~zeilberg/EM20/Lambert.pdf)

- [Monte Carlo Statistical Methods](https://mcube.lab.nycu.edu.tw/~cfung/docs/books/robert2004monte_carlo_statistical_methods.pdf)
- [Markov Chain Monte Carlo Lecture Notes - Geyer 2005](https://www.stat.umn.edu/geyer/f05/8931/n1998.pdf)
- [Simple example: Linear Regression using bayesian statistics Metropolis-Hastings MCMC in R](https://khayatrayen.github.io/MCMC.html)
- [Gibbs sampler with R](https://malouche.github.io/BayesianStatistics/Gibbs_sampler.html)