Python notebooks about decision theory algorithms
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Updated
Dec 8, 2022 - Jupyter Notebook
Python notebooks about decision theory algorithms
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.
This notebook analyzes Nobel Prize data from 1901 to 2016, exploring factors such as gender, nationality, age, and repeat laureates. It examines trends and patterns, revealing insights about the Nobel Prize's evolution over time, including the dominance of the United States and the representation of women in different prize categories.
A Jupyter notebook for a project centered around 'Group Recommendation Systems (GRS)' utilizing the 'GcPp' clustering approach.
An LP problem with hiearchical objectives, modeled as a vehicle routing problem , implemented with gurobipy python API in a jupyter notebook, and solved by using the Gurobi solver
Reinforcement Learning and Decision Making tutorials explained at an intuitive level and with Jupyter Notebooks
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