Perform distribution analysis on heavy-tailed distributed data

A python module for the simulation of aroma transport processes during conching of dark chocolate

Uncertain parameter estimation on Grey-Box Dynamic Systems

IME-published article on Long-term Real Dynamic Investment Planning. While we enhance predictability of the real returns of S&P500 Index, we derive optimal non-myopic investment strategy, and we compare its performance with near-optimal Dynamic and Constant Merton investment strategies.

Documentation of the Generalized Weibull Regression REU Project completed at the University of Michigan Dearborn Summer 2023

OLS. R and Python. In this project, we study fundamental concepts of Supervised ML models, such as Regression Analysis: Coefficient of Model Adjustment (R²), Parameters Estimation ,Statistical Significance of the Model (F test, T test) ,Multiple Regression , Qualitative Explanatory Variables (X) , heteroscedasticity and etc.

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.

Study of the COVID-19 epidemic in Cameroon using a SIRD model with population birth and death rates.

Projects for Systems Modeling & Simulation Course / Aristotle University of Thessaloniki / Summer Semester 2021

My first attempt optimizing the design parameters of a permanent magnet synchronous motor using an FE development tool

Analyze the performance of thousands of students with just one sample. Determine the socioeconomic variables that increase a student's performance and graphically visualize the results

Assignments completed for my Machine Learning course: Topics include probability and statistics proofs, MLE/MAP parameter estimation, EM Algorithm, Bayes Theorem implementations, gradient descent methods, Neural Networks and Deep Learning.

Simulation of an epidemic on a random graph of choice according to discrete-time SIR and SIRV models and estimation of parameters by gradient descent

Code for the paper "Quantum Targeted Energy Transfer through Machine Learning Tools" by Iason Andronis and George Arapantonis

Parameter Estimation is a branch of statistics that involves using sample data to estimate the parameters of a distribution.

Reduced Dimension Ensemble Modeling and Parameter Estimation

A very simple SIR model implementation and Levenberg-Marquardt fit with the Johns Hopkins CSSE data for COVID-19.

This repository contains comprehensive information on the steps necessary for parameter selection and statistical tests and model selection