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a-simple-metropolis-hastings-mcmc-in-r

adaptmcmc

R package that provides an implementation of the generic adaptive Monte Carlo Markov chain sampler proposed by Vihola (2011).

adaptmcmc-1

:exclamation: This is a read-only mirror of the CRAN R package repository. adaptMCMC — Implementation of a generic adaptive Monte Carlo Markov Chain<U+000a>sampler

big-data2

The First Project

bigdata_aims_senegal_ii

Repository containing slides and material for the "Big Data II" lecture at AIMS Senegal.

branchingprocess

Some functions to simulate branching process(with immigration) and branching random livin in random environment, Harris tree, etc...

customersegmentation_amadeus

The objective of market segmentation is to divide a broad target market of customers into smaller, more similar groups and then design a marketing strategy specifically for each group. Clustering is a common technique to automatically find similar groups within a given data set. In this analysis, we will use clustering to find similar groups of customers within an airline's frequent flyer program, so that the airlines can target different customer segments with different types of mileage offers.

gibbs-sampler

A probabilistic MCMC algorithm to find motifs in DNA

hastings

To test the sampling methods "Metropolis Hastings Markov Chain Sampling"

hoppmcmc

An adaptive basin-hopping Markov-chain Monte Carlo algorithm for Bayesian optimisation

magro

MCMC Another Gibbs Sampler

metropolis-hastings-random-walk-and-re-weighted-random-walk

a miniproject submitted from a Master course (ID2220 Advanced Topic in Distributed Computing). The goal is to perform Metropolis-Hastings random walk (MHRW) and re-weighted random walk (RWRW) on an offline Gnutella dataset.

metropolishastingssimple.jl

mhadaptive

:exclamation: This is a read-only mirror of the CRAN R package repository. MHadaptive — General Markov Chain Monte Carlo for Bayesian Inference using<U+000a>adaptive Metropolis-Hastings sampling

mhs

Metropolis Hasting Implementation

modelselection_fda

Here, our project deals with the selection model when we have functional data by using the model-based clustering method, called FunFEM. And we apply our study on CO2 Emissions Dataset (Data from The World Bank) to cluster the countries according to CO2 emission in the world.

naive-bayes-gibbs-sampler

Gibbs sampler for for a Naive Bayes document classifier

r-proto-gibbs

Gibbs sampler in R with proto

rshiny

Project Mosaic workshop

sklearn_tutorial

Tutorial for astronomy data processing with scikit-learn

sklearnflask

Flask API for training and predicting using scikit learn models

stata-amcmc

Stata and Mata code for adaptive Markov chain Monte Carlo simulation; Stata code for Bayesian estimation of mixed logit models and truncated quantile regression models