Lectures on Bayesian statistics and information theory
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Updated
Sep 16, 2021 - Jupyter Notebook
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
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
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.
Labs for "Decision theory" course of BMSTU Software Engineering first year
Simulation of agents competing for resources with different strategies
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
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