Implementation of a parameter estimation method without bias, applied to the PUMP problem
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Apr 24, 2020 - Jupyter Notebook
Implementation of a parameter estimation method without bias, applied to the PUMP problem
Ising Model Simulation & lab report with applications to epidemiology. COVID-19 is a case study.
732A64 Master Project
Here, I tried to learn some Markov chain Monte Carlo methods.
Personal Website with Blogposts, Achievements and Ideas
A Bayesian approach to the modeling COVID19 spread based on the Gompertz equation applied to the confirmed cases and fatalities by country.
Predicting property prices in Melbourne, using Bayesian MultiRegression and Markov Chain Monte Carlo Simulations.
My FSharp Advent 2020 submission related to conducting Bayesian Inference in F#
Causal Effects in Principal Strata Defined by Antidrug Antibodies
Creating plots illustrating the SED of PKS 1510-089 for the Treball Fin de Master at Universitat Autonoma de Barcelona to complete the master degree in HEP, Astrophysics and Cosmology at IFAE.
Use this to determine the optimal route to go on a search for shortage struck essential commodities (gasoline, water, toilet paper etc.) using information from social media
Repostory containing a classical simulator of the first version of the Quantum-enhanced MCMC optimization algorithm.
Forward kinematic modeling of disk galaxies in MaNGA via parallel-tempered MCMC
Hand-made R Functions Mainly for Statistical Data Analysis
Local-Global MCMC kernels: the best of both worlds (NeurIPS 2022)
Sample R code from homework problems in STATS 102C at UCLA, Intro to Monte Carlo Methods.
A collection of MCMC methods in Python using Numpy and Scipy
Code implementations of the methods discussed in Generalized Fiducial Inference on Differentiable Manifolds by A. Murph, J. Hannig, and J. Williams.
Using the Ising Model and a Monte-Carlo Markov Chain Approach to Illustrate Magnetic Phase Transition
This repository provides a package that allows the implementation of Conditional Particle Filter easily. Conditional Particle Filter can be viewed as an MCMC method with invariant distribution as the smoothing distribution of a partially observed diffusion model.
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