[SOLVED] CSE571 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​.
**You can find the documents for pgmpy on the ​pgmpy Documentation Page​.

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.