Lectures on Bayesian statistics and information theory

🎓 Uni-Bonn Decision Analysis graduate course, lectures and materials

Bayesian Information Gap Decision Theory

EE7403 Image Analysis & Pattern Recognition

📦 A library for solving POMDPs

The project is related to the development of labs for the ITMO Speaker Recognition Course.

This repository holds the code for our paper "Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis" by Alexander Tornede, Marcel Wever, Stefan Werner, Felix Mohr and Eyke Hüllermeier.

📈 Laboratory works on the subject "Economic decision theory"

🧮 🔢 ➕ Projects based in Introduction to Operational Research Labs. This projects was built using Jupyter Notebook, iPython, Python, Anaconda, Spyder IDE, JetBrains PyCharm and MATLAB. This repository it's based in some practical lab exercises and examples related with Introduction to Operational Research, using some software and libraries like M…

The Sleeping Beauty problem is a puzzle in decision theory

All type of calculators like Cuboid (4D), Binning, Chi-square test, Red-black tree, Binary search tree, Longest Common Sub Sequence, Master Theorm, Heap Sort, Decision Theory at one place ✨

A multi-agent hide and seek on Minecraft played by AIs

Automated Analysis of Decision Rules

Labs for "Decision theory" course of BMSTU Software Engineering first year

Simulation of agents competing for resources with different strategies

Efficient Government Strategies: Research findings on revolution, legal aspects, and societal satisfaction, supported by literature analysis and a Python-powered survey approach. Explore results and documentation in the attached files.

A simulation of a game that has characteristics similar to The Secretary Problem, but where the numbers are generated in a specific, known way

Criteria for decision-making in games against nature under uncertainty in form of Jupyter Notebook. Supports Wald's, Savage, Hurwicz, Hodges–Lehmann, Laplace, Maximum Expected Utility, Minimum Expected Regret criteria.

Learned the fundamentals and applications in ML: Intro to Prob. & Linear algebra, Decision Theory, MLE & BE, Linear Model, Linear Discriminant function, Perceptron, FLD, PCA, Non-parametric Learning, Clustering, EM, GMM, EM and Latent Variable Model, Probabilistic Graphical Model, Bayesian Network, Neural Network, SVM, Decision Tree and Boosting

Diversified Farming Systems -- Markov Decision Process Model