CSE571 Bayesian Networks Project 1 -Solved

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:  

1.    Familiarize with the Bayesian Model (BN) class in pgmpy library. An example (bn.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:

a.    P(D|-c) = {0.65, 0.35}

b.    P(C|-s, -p) = {0.97, 0.03}

 
2.    Answer the following questions using the provided code and​   by hand to see whether​   they match (this question is not graded):

a.    P(+d|+s)

b.    P(+x|+d,-s)

c.     Does pgmpy return exact results (up to the system’s accuracy)?

3.    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):

a.    P(+j|-e) 

b.    P(+m|+b,-e)    

c.     P(+m|+b,+e)   

d.    P(+m|+j)          

e.    P(+m|+j,-b,-e)

 
5.    Familiarize with the Dynamic Bayesian Model (DBN) class in pgmpy library. An example (dbn.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:

a.    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)

 6.    Create code for the DBN for the following problem, which is similar to the problem discussed in our DBN lecture:

  a.    You agent always move in a clockwise fashion

b.    When it moves, it has a 50% chance of moving to the desired location and 50% it stays where it was.

c.     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

d.    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.  

Submission Directions for Project Deliverables 

Submission templates and resources for this project can be found in the “CSE 571_Bayesian Networks_Project Templates and Resources” for you to download.  

Files to submit:

●     burglary.py

●     agent.py