[SOLVED] CSE571 Bayesian Networks

Description

• Create Bayesian Networks.
• Determine inferences from Bayesian Networks.
• Model real-world problems in Bayesian Networks.

Technology Requirements: 

• Linux (windows user may install virtual machines)
• Python 3.4 or higher
• Download and install pip and then install pgmpy:

○    $ git clone https://github.com/pgmpy/pgmpy

○    $ cd pgmpy/

○    $ sudo pip install -r requirements.txt

○    $ sudo python setup.py install

*Note:​ if you encountered problems installing pip or pgmpy, refer to the pgmpy

Installation Page: https://github.com/pgmpy/pgmpy#installatio​ n

**You can find the documents for pgmpy on the pgmpy Documentation Pag​ e.​

Project Description: 

Familiarize with the Bayesian Model (BN) class in pgmpy library. An example (py​ )​ illustrating BN construction an inference is provided for the following BN:

Run bn.py by “python bn.py”. The following shows you the results of two queries:

3. P(D|-c) = {0.65, 0.35}
4. P(C|-s, -p) = {0.97, 0.03}
5. Answer the following questions using the provided code and​ by hand to see whether​            they match (this question is not graded):
   1. P(+d|+s)
   2. P(+x|+d,-s)
   3. Does pgmpy return exact results (up to the system’s accuracy)?
6. Create code for the following BN:

Artificial Intelligence: A Modern Approach​ 3rd Edition​.

Save it as “burglary.py​      ”. Important: please follow the instructions in the template provided to​           you to name your variables and structure your code.

4. Answer the following questions using your code and​ by hand to see whether they match​  (this question is not graded but the code output should match with your computation by hand):
   1. P(+j|-e)
   2. P(+m|+b,-e)
   3. P(+m|+b,+e)
   4. P(+m|+j)
   5. P(+m|+j,-b,-e)

5. Familiarize with the Dynamic Bayesian Model (DBN) class in pgmpy library. An example (py​ )​ illustrating DBN construction an inference is provided for the following DBN:

Run dbn.py by “python dbn.py”. The following  shows you the results of a query:

1. P(G3|g0=1, g1=2) = {0.4358, 0.2552, 0.3090} ​ (the distribution of G at the 3rd​ time slice given g0=1 at the zeroth step and g1=2 at the first step)
2. Create code for the DBN for the following problem, which is similar to the problem discussed in our DBN lecture:
3. You agent always move in a clockwise fashion
4. When it moves, it has a 50% chance of moving to the desired location and 50% it stays where it was.
5. The robot is equipped with a sensor that returns the correct position with a 60% chance and a random position (including the correct position) with a 40% chance
6. The agent starts at C at time 0.

Save it as “agent.py​     ”. Important: please follow the instructions in the template provided​ to you to name your variables and structure your code.

7. Test your code thoroughly.

For example, P(Location1 = A | Sensor 1=C)= 0.125   (The probability of the agent at location A at step 1 given that the sensor at step 1 returns C.