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Modelled COVID-19 pandemic with a system of 9 first order differential equations. The system was fitted to the values of the pandemic in Italy, UK, India, Brazil and Sweden, and numerically solved using MCMC statistical methods in python’s lmfit module. Estimates of the real number of infected people and predictions for the future were then made.
This project implements a decoding algorithm using MCMC (Markov Chain Monte Carlo) methods in R. The approach leverages probabilistic sampling to estimate hidden states in a sequence, commonly used in applications like hidden Markov models and Bayesian inference. The code includes data preprocessing, model setup, and result.
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.
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.
This module is an efficient and flexible implementation of various Sequential Monte Carlo (SMC) methods. Bayesian updates occur for both latent states and model parameters using joint inference.
Repository for example Hierarchical Drift Diffusion Model (HDDM) code using JAGS in R. These scripts provide useful examples for using JAGS with R2jags, the JAGS Wiener module, mixture modeling in JAGS, and Bayesian diagnostics in R.
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