A C++ and WPF implementation of Computer-Graphics-Principles And Practice by JOHN F. HUGHES, ANDRIES VAN DAM, MORGAN MCGUIRE, DAVID F. SKLAR, JAMES D. FOLEY, STEVEN K. FEINER and KURT AKELEY

Homeworks on "Computational Statistics" course

Sample from synthetic covariate shift problem

Acceptance and rejection method

implement sampling methods

Recursos sobre manejo de la incertidumbre y probabilidad por un agente inteligente, módulo de Modelos de Inteligencia Artificial

This project implements both exact and approximate inference techniques for Bayesian Networks using enumeration and rejection sampling, respectively. It processes Bayesian Network structures in XMLBIF format, accepting command-line inputs to compute the posterior distribution of a query variable given observed evidence.

Application of rejection sampling and markov chain monte carlo (MCMC) algorithms to approximate bayesian computation (ABC). The project includes application of ABC to model the pharmacokinetics of theophylline.

Some implementations of partial rejection samplers

Compares four different methods for generating uniformly distributed random points within a unit circle

Monte Carlo methods with TensorFlow

Acceptance-Rejection sampling with examples in R and Python

Poker test for independence, Inversion method, Method of approximations, Rejection method, Quadratic congruent random number generator, Freedman–Diaconis rule, Fixation Index, Extended Haplotype Homozygosity, Wright-Fisher model

The programming part for the second assignment of the course DSC 531 - Statistical Simulations and Data Analysis of the University of Cyprus MSc in Data Science programme

We implement the Schnorr proof system in assembler via the Jasmin toolchain, and prove the security (proof-of-knowledge and zero-knowledge) and the absence of leakage through timing side-channels of that implementation in EasyCrypt.

A Bayesian network calculator for both exact (enumeration) and approximate inference (rejection sampling, likelihood weighting).

Artificial Intelligence Projects - Sharif University of Technology - Fall 2020

Monte is a set of Monte Carlo methods in Python. The package is written to be flexible, clear to understand and encompass variety of Monte Carlo methods.

Implementation of self-organizing maps for mesh generation

Implementation of Prior, Rejection, Likelihood and Gibbs Sampling