estimators

TransmogrifAI (pronounced trăns-mŏgˈrə-fī) is an AutoML library for building modular, reusable, strongly typed machine learning workflows on Apache Spark with minimal hand-tuning

Actuarial reserving in Python, triangle data manipulation, link ratios calculation, and IBNR models.

Python implementation of Double/Debiased machine learning algorithm from Chernozhukov et. al ontop of sklearn. Builds and deploys to pip using github actions.

A Python implementation of the semiparametric double-index estimator of Klein and Vella (2009)

A tensorflow template with TF Dataset API, TFRecords and TF Custom Estimator for multi-class image classificaiton task

A comprehensive open-source library of general numerical algorithms for embedded applications.

sequential state estimator examples, including EKF, UKF, CKF, IMM, and etc.

A modular two-step convex optimization estimator for ill-posed problems

A Modular Two-Step Convex Optimization Estimator for Ill-Posed Problems

A collection of academic exercieses and exams related to Probability and Statistics

Implementation of the projects for the DSC 530: Probability and Statistics for Data Science course, of the MSc in Data Science programme of the University of Cyprus.

Event Planning Redefined. The ultimate suite of 1,500+ professional tools for invitations, checklists, and budgeting. 100% free, private, and runs entirely in your browser.

A machine learning project designed to predict the likelihood of heart disease based on a set of health indicators.

Review of bootstrap principles and coverage analysis of bootstrap confidence intervals for common estimators

R simulations illustrating the fundamental properties of statistical estimators, including unbiasedness, consistency, and efficiency through Monte Carlo experiments and inferential statistics

Labs and demos for courses for GCP Training (http://cloud.google.com/training).

This repository contains Python implementations of tables and figures from the reference textbook !Robust Statistics Theory and Methods!. The goal is to provide reproducible, executable code for visualizations and results presented in the book.

Properties of estimators using the Univariate Normal model

Extra blocks for scikit-learn features.

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