Formative research work of the course Introduction to statistics and probabilities.

Implemented normalized, polar and delta feature sets, cross validation folds, Bayesian Information Criterion and Discriminative Information Criterion model selectors, as well as the recognizer in order to detect and translate sign language into text using hidden markov models as part of the Udacity Artificial Intelligence Nanodegree.

Jupyter notebooks for probabilistic modelling of vibrational spectroscopic datasets

Model to evaluate the distribution of Regular Expressions (REs) across a ranked sequence list.

This repository is intended for documenting Team 18's codes and outputs for the ANLY511 Project.

Information-theoretic Approaches to Transit Equity. Use information-theoretic invariants (entropy) to quantify inequity in walk/transit access. Evaluate how effective these invariants are (e.g. noise- and parameter-robustness, computability, intrinsic features, compare to other measurements).

Species tree aware simultaneous reconstruction of gene and domain evolution

A reproduced research on HIV/AIDS mortality in order to investigate how the model covariates poverty, income inequality and spatiotemporal effects influence the model fit.

Project in IR

Probabilities calculated in the probability and contingency fields, explaining the concept of Bayes' theorem.

A machine learning & deep learning-based probabilistic time series forecasting project to predict pedestrian flow in Würzburg. Uses LSTM, CatBoost, and LightGBM to optimize forecasting accuracy.

This repository contains work completed as part of the "Applied Probabilistic Models" course. The practical sessions focus on Bayesian inference and probabilistic modeling, covering topics such as simulation, change point detection, and predictive decision-making, all implemented in R.