Instructed by : Prof. Manisha Pal. A repository created with the practical problems on Bayesian computing and some advance computing related to MCMC, Metropolis etc.
-
Updated
Nov 26, 2023 - R
Instructed by : Prof. Manisha Pal. A repository created with the practical problems on Bayesian computing and some advance computing related to MCMC, Metropolis etc.
A friendly MCMC framework
Monte Carlo Markov Chain algorithms in R and Python
Bayesian Regression Analyses from scratch - NBA data example
CUHK Course code: STAT 3011 | This course is designed to strengthen students' ability in statistical computing as well as in processing and analysing data. Students are required to participate in several term projects with emphasis on techniques of data management and analysis.
Monte Carlo Penalty Selection for graphical lasso
This repo contains the codes in R and Cpp to replicate the original proposal of Linkletter for Bayesian Spatial Process Models for Social Network Analysis and our proposal using an estimation of the likelihood function.
This repo contains the codes, images, report and slides for the project of the course - MTH516A: Non-Parametric Inference at IIT Kanpur during the academic year 2022-2023.
Metropolis and Nested Sampling in R
Gibbs sampler implementation of a hierarchical Bayesian model for the analysis of double pass data
MCMC with R
Simulation of random numbers using Metropolis Hastings MCMC technique/algorithm
Add a description, image, and links to the metropolis-hastings topic page so that developers can more easily learn about it.
To associate your repository with the metropolis-hastings topic, visit your repo's landing page and select "manage topics."