Course materials for Computational Statistics, PhD course at EMAp.

Bayesian spatio-temporal methods for small-area estimation of HIV indicators (PhD thesis, Imperial College London, 2023)

Class notes for the computational statistics class (Spanish), master in Data Science ITAM

CE083 - Estatística Computacional I

Forward-backward conditional sampling

Fast Weight of Evidence (WOE) encoding and inference

An introduction to computational statistics with examples and comparison to analytical methods

Python implementation (from scratch) of some MCMC samplers that can leverage pyTorch's autodifferentiation (with examples).

Algorithms and case studies for the paper "Accelerating delayed-acceptance Markov chain Monte Carlo algorithms".

Computational Statistics, STAT525, 2020Spring, UIUC

Markov Chain Monte Carlo(MCMC), Approximate Bayesian Computation(ABC), Bayesian Synthetic Likelihood(BSL), Variational Inference(VI)

Python Library for Hamiltonian Markov-Chain Monte-Carlo in Computational Statistics (pyHaiCS).

Code for the nested Gaussian filters (NGFs), in particular, an implementation of an unscented Kalman filter (UKF) combined with a bank of extended Kalman filters (EKFs). Other algorithms are implemented to compare performance.

Bayesian parameter estimation of HMMs in Julia

Code for the nested hybrid filters (NHFs), including four different implementations using sequential Monte Carlo (SMC), sequential quasi-Monte Carlo (SQMC), extended Kalman filters (EKFs) and ensemble Kalman filters (EnKFs). I have also included the implementation of the nested particle filter (NPF) and the two-stage filter to compare performance.

Work from the PhD-Level Computation Statistics Course from FSU taken Spring 2019

Looking for the number of sticker packages one should buy in order to fully complete Panini's World Cup Album 2022.

🅰️ 🅱️ ©️ An Implementation of the ABC permutation test using R and C++ for Computational Statistics course CM0527

Comprehensive notes and implementations from Monte Carlo Statistical Methods (2nd Ed.), focusing on Bayesian inference, filtering, smoothing, and state estimation using Monte Carlo and MCMC techniques.

Work done for the Fall 2018 class of Computational Statistics at ENSAE