Course materials for Computational Statistics, PhD course at EMAp.
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Updated
Feb 15, 2024 - TeX
Course materials for Computational Statistics, PhD course at EMAp.
Class notes for the computational statistics class (Spanish), master in Data Science ITAM
CE083 - Estatística Computacional I
Bayesian spatio-temporal methods for small-area estimation of HIV indicators (PhD, Imperial College London, 2023)
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
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.
Looking for the number of sticker packages one should buy in order to fully complete Panini's World Cup Album 2022.
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.
Work done for the Fall 2018 class of Computational Statistics at ENSAE
Markov Chain Monte Carlo(MCMC), Approximate Bayesian Computation(ABC), Bayesian Synthetic Likelihood(BSL), Variational Inference(VI)
Computational Statistics
Extremely fast and scalable algorithms for phase-type distributions (including discrete, multivariate, rewarded, and time-inhomogeneous). Interface to both C and R
This is a repository for Yale course BIS 557 Computational Statistics.
Repository hosting website for the Neural Networks reading group at the University of Bristol.
A Python framework for working with random variables
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