https://github.com/abhishekshree/mcmc-methods-in-julia
https://github.com/abhishekshree/mcmc-methods-in-julia
Last synced: 9 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/abhishekshree/mcmc-methods-in-julia
- Owner: abhishekshree
- Created: 2022-06-30T16:35:35.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2022-06-30T17:01:55.000Z (over 3 years ago)
- Last Synced: 2025-01-21T09:48:57.308Z (11 months ago)
- Language: Jupyter Notebook
- Size: 1.2 MB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# MCMC-Methods-in-Julia
Project done under Stamatics, 2022.
Resources Used: [MCMC Methods in Julia (Resources)](https://docs.google.com/document/d/1eioUEP7i1V89BIXY8u6L3qkvIZchk63ukC6tHJMiSrw/edit?usp=sharing)
### Work done before:
- __Assignment 1__: Understood methods to generate samples in case of discrete distributions like Accept-Reject, Inverse-Transform, etc.
- __Assignment 2__: Understood methods to generate samples in case of continuous distributions and some miscellaneous methods. Proved the correctness of the Bernoulli Factories.
- __Assignment 3__: Worked on Importance Sampling Methods, Maximum Likelihood Estimation, Regression, Bayesian Models and Acceptance-Reject for Bayesian Models.
- __Assignment 4__: Worked on understanding the mathematics behind Markov Chains, MCMC, Metropolis-Hastings, and Introductory Measure Theory. Implemented the MH algorithm for various distributions.
- __Assignment 5__: Learnt about Stochastic Processes and Stability, different MH Algorithms, and Gibbs Sampling. Summarized and implemented portions of the paper titled [Efficient Bernoulli factory MCMC for intractable posteriors](https://arxiv.org/abs/2004.07471) consisting of the Barker's Method and portkey Barker's algorithms (computationally more efficient that the current state-of-the-art).