City-scale car traffic and parking density maps from Uber Movement travel time data
-
Updated
Feb 7, 2023 - Julia
City-scale car traffic and parking density maps from Uber Movement travel time data
MCMC Simulation of Hard Disks
Homework Solutions for Stochastic Processes Course as Computer Science B.Sc. Student at Department of Mathematical Sciences, Sharif University of Technology
MC3 RWMH using MPI.
Markov Chain Monte Carlo Simulation for COVID-19 Incidence.
Code and data for my project on Markov Chain Monte Carlo (MCMC) simulations applied to analyze the behavior of different customer types and their impact on traffic and congestion levels in supermarkets. The project aims to provide insights into the dynamics of customer behavior and its implications for supermarket operations.
My learning journal studying Markov-Chain Monte Carlo methods
A Python package for MCMC sampling.
Code Repository for BGCN-NRWS (IEEE ICMLA)
a visualization of the metropolis-hastings algorithm, a markov chain monte carlo method which utilizes dependent sampling for high-dimensional distributions
Source code for the paper "An MCMC based course to teaching assistant allocation".
Markov Chain Monte Carlo
Universal Programmable Inference in JAX
Using Markov-Chain Monte-Carlo algorithms to find patterns with specific properties in the game of life - A new approach to balance exploration vs. exploitation
Markov Chain Monte Carlo on graph space applied to the study of neuronal interactions from experimental data
This is a python package based on the source code for "An MCMC Based Course to Teaching Assistant Allocation", S. Kumar, S. Moothedath, P. Chaporkar, M. Belur, Proceedings of the Fifth International Conference on Network, Communication and Computing (2016). ACM.
A review of Silverman (2020) "Multiple-systems analysis for the quantification of modern slavery: classical and Bayesian approaches"
My personal repository for Statistical Thinking course assignments, 8th semester, Computer Science Engineering.
A application written in C that generates a maze at random using two methods: Markov-Chain-Montecarlo and Hillbert Lookahead. And then solves the maze concurrently
Add a description, image, and links to the markov-chain-monte-carlo topic page so that developers can more easily learn about it.
To associate your repository with the markov-chain-monte-carlo topic, visit your repo's landing page and select "manage topics."