MDP and Monte Carlo solution for maze solving
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Updated
Aug 27, 2020 - Python
MDP and Monte Carlo solution for maze solving
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.
A brief introduction to Markov chain Monte Carlo methods
Clustering users into different groups based on click logs of news articles
G-PhoCS is a software package for inferring ancestral population sizes, population divergence times, and migration rates from individual genome sequences.
Particles, a self-organizing particle system algorithm simulator, developed for research projects at GT's Theory Lab.
PyTorch implementation of Bayesian Graph Convolutional Networks using Neighborhood Random Walk Sampling to supplement my Honors Thesis.
This repo contains Markov Chain Monte Carlo (MCMC) implementation of wake / sleep model by using Pyro. I've re-implemented the work done with pymc3 on this blog earlier. ( https://towardsdatascience.com/markov-chain-monte-carlo-in-python-44f7e609be98 )
Implementation of a coupled Metropolis-Hasting Algorithm in Jax. Project done as part of the Bayesian Machine Learning course by Rémi Bardenet and Julyan Arbel.
A C++ header only library that performs Markov chain Monte Carlo sampling in several forms.
Open access PBPK modelling platform
This repository contains code for the paper `Sequential Monte Carlo algorithms for agent-based models of disease transmission' by Nianqiao (Phyllis) Ju, Jeremy Heng and Pierre Jacob.
An application of Markov Chain Monte Carlo to 2-d Ising system.
Python library for the evaluation of simulation data. The library provides functionalities to load simulation results into Python, to perform standard evaluation algorithms for Markov Chain Monte Carlo algorithms. It further can be used to generate a pytorch dataset from the simulation data.
Metropolis Light Transport (Reading Group)
Correlated pseudo-marginal Metropolis-Hastings using quasi-Newton proposals
Implementation of Markov chain Monte Carlo sampling and the Metropolis-Hastings algorithm for multi-parameter Bayesian inference.
Coursework completed for Mathematical Inverse Methods in Earth and Environmental Sciences -- Integrated Data Analytics II. Focus on data-driven modeling to solve inverse problems and estimate model parameters.
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