Assignment: Probability
Abstract: You will implement several Bayesian networks and sampling algorithms to gain a better understanding of probabilistic systems.
Learning Objectives: Students should be able to understand the importance of Bayesian networks to represent conditional dependencies. Also, be able learn the sampling methods, Gibbs and Metropolis-Hastings and develop an intuition for their convergence criteria (very "researchy").
Evaluation: Evaluation is using the last submission on Bonnie.
The Challenge -
Many AI systems rely on probabilistic knowledge of the world, rather than absolute knowledge, to execute tasks efficiently: for example, motion planning in robots with unreliable sensors. One type of probabilistic system that is especially useful is the Bayesian network, which encodes a joint probability distribution among dependent variables as a network of conditional probabilities. Your challenge is to implement and test several of these networks, ultimately using a sampling method to approximate a probability distribution.
Your Assignment -
Your task is to implement a few basic networks as well as several sampling algorithms. You will do this in probability notebook.ipynb, and there are tests along the way to help. Unlike previous assignments, we will not be grading on performance but rather on completion.
We have provided the following additional classes and files: (GitHub Repo):
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File/Folder
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Description
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Probability_tests.py
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To test the models you've built.
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Pbnt/combined
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Module to implement Bayesian networks (you'll basically need BayesNode in Node.py and BayeNet in Graph.py).
Also contains an example (ExampleModels.py) to help you get started.
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This is meant to be a shorter assignment, so there won't be much testing required.
Attachment:- Assignment File.rar