CSE643 AI ASSIGNMENT 3 Solution

Theory
(ii) (1 mark) Verify that these propositions create a valid probability distribution. List the set of axioms that they satisfy.
(iii) (1 mark) Populate the full joint probability distribution table.
(a) About 82.5 % people have travelled and have caught either corona or other diseases.
(b) Of the people who had travelled 15 % have mild and 22 percentage have severe cases of corona, respectively.
(c) Given that a person travelled the chance they caught a disease other than corona is 0.485 rounded to 3 decimal places.
(d) About 24 % of people died of diseases other than corona after travelling.
(e) There is 0.025 probability that a person has not travelled and has severe case of corona.
(f) Given a person has not travelled the probability that the person is severely sick is about 0.457 rounded to 3 decimal places.
(g) The probability that a person has died and did have corona is 0.059.
(h) About 70 % people had mild or severe cases of any disease.
(i) There is 80 % chance that a person has travelled given that he is severely sick. (j) There is 50 % chance a person had corona whether they travelled or not.
(a) (1 mark) Should you switch your choice to the other unopened door to maximize your chance of winning the key?
(c) (1 mark) If you choose to switch, what is the conditional probability that you win the key if the man has mistakenly revealed the door that shows life lost?
(d) (1 mark) Additionally, what is the conditional expectation of your prize (key/life lost) based on your choice to switch or stick, considering both possible scenarios? Would you choose to switch or stick based on the conditional expectation?
Computational
1. Create a Bayesian Network (14) :
(a) (2 mark) Dataset : Load the wine quality dataset https://archive.ics.uci.edu/ dataset/109/wine. The dataset is continuous and therefore discretization would be required to build the network. You can explore continuous/hybrid models also. Build classification model based on class variable in the data for performance evaluation (accuracy) https://scikit-learn.org/stable/modules/generated/sklearn.metrics. accuracy score.html of the network. You can use any available open-source packages to build the network, example bnlearn package {https://pypi.org/project/ bnlearn/}.
(b) Instructions to Construct the network: You will need to construct and evaluate a total of three networks A, B and C as described below:
• (2 mark) Construct a Bayesian network (A) for the data. Visualise the network and the probability distribution. Describe a few examples of parent and child nodes.
• (1 mark) Prune the network (A) for better performance on the class variable. Let the new network be (B). Explain your method of pruning.
• (1 mark) Compare the prediction performance based on the three models (A), (B) and (C).
• There will be relative grading based on your explanation and innovative implementations.
(c) Instruction for submission : Please follow the guidelines below to submit the findings. There are penalties for not following the format outlined below.
• (-1 mark) The data should be imported correctly, and any pre-processing should be explained. Otherwise explain why there was no pre-processing.
• (-1 mark) Make a proper pdf file with all the results.
• (-3 mark) Make a table with the performance measures of the three Bayesian networks.
• (-1 mark ) The explanations and descriptions should be proper.
• (-1 mark) The code is incorrect.
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