Projects

1. Implement a program, which
2. Which on input the MN DAG (as adjacency matrix), and samples from the joint/and/or marginal distribution, learns the joint distribution.
3. Which on input the MN DAG and the joint distribution, and a conditional probability query, outputs the probability The project should be made into a public github repo, with proper readme files.

• Bayes Ball

  1. Which on input X,Y,Z, checks conditional independence using d-seperation. All examples on the textbook need to be given as test cases.
  2. Linear Time Bayes Ball algorithm and analysis with latex scribe.

• Prove theorems regarding equivalence of Markov Networks. Equivalence of CI statement and d-seperability.

• IMNet : https://pdfs.semanticscholar.org/6314/2e3f05bfc1e1394a0f46af341837a40fe3b8.pdf

• Ancestral Graph Markov Model: https://projecteuclid.org/download/pdf_1/euclid.aos/1031689015

• Causality and do calculus (Pearl[220])

• Expert Systems in Medical Field: https://projecteuclid.org/euclid.ss/1177010888