Description
Task 1
50 points
The task in this part is to implement a system that:
Can determine the posterior probability of different hypotheses, given priors for these hypotheses, and
given a sequence of observations.
Can determine the probability that the next observation will be of a specific type, priors for different
hypotheses, and given a sequence of observations.
As in the slides that we saw in class, there are five types of bags of candies. Each bag has an infinite amount of
candies. We have one of those bags, and we are picking candies out of it. We don’t know what type of bag we
have, so we want to figure out the probability of each type based on the candies that we have picked.
The five possible hypotheses for our bag are:
h1
(prior: 10%): This type of bag contains 100% cherry candies.
h2
(prior: 20%): This type of bag contains 75% cherry candies and 25% lime candies.
h3
(prior: 40%): This type of bag contains 50% cherry candies and 50% lime candies.
h4
(prior: 20%): This type of bag contains 25% cherry candies and 75% lime candies.
h5
(prior: 10%): This type of bag contains 100% lime candies.
Command Line arguments:
The program takes a single command line argument, which is a string, for example CLLCCCLLL. This string
represents a sequence of observations, i.e., a sequence of candies that we have already picked. Each character is
C if we picked a cherry candy, and L if we picked a lime candy. Assuming that characters in the string are
numbered starting with 1, the i-th character of the string corresponds to the i-th observation. The program should
be invoked from the commandline as follows:
compute_a_posteriori observations
For example:
compute_a_posteriori CLLCCLLLCCL
We also allow the case of not having a command line argument at all, this represents the case where we have
made no observations yet.
Output:
Your program should create a text file called “result.txt”, that is formatted exactly as shown below. ??? is used
where your program should print values that depend on its command line argument. Five decimal points should
appear for any floating point number.
Observation sequence Q: ???
Length of Q: ???
After Observation ??? = ???: (This and all remaining lines are repeated for every observation)
P(h1 | Q) = ???
P(h2 | Q) = ???
P(h3 | Q) = ???
P(h4 | Q) = ???
P(h5 | Q) = ???
Probability that the next candy we pick will be C, given Q: ???
Probability that the next candy we pick will be L, given Q: ???
Task 2
50 points
Figure 1: A Bayesian network establishing relations between events on the burglary-earthquake-alarm
domain, together with complete specifications of all probability distributions.
For the Bayesian network of Figure 1, implement a program that computes and prints out the probability of any
combination of events given any other combination of events. If the executable is called bnet, here are some
example invocations of the program:
1. To print out the probability P(Burglary=true and Alarm=false | MaryCalls=false).
bnet Bt Af given Mf
2. To print out the probability P(Alarm=false and Earthquake=true).
bnet Af Et
3. To print out the probability P(JohnCalls=true and Alarm=false | Burglary=true and Earthquake=false).
bnet Jt Af given Bt Ef
4. To print out the probability P(Burglary=true and Alarm=false and MaryCalls=false and JohnCalls=true and
Earthquake=true).
bnet Bt Af Mf Jt Et
In general, bnet takes 1 to 6(no more, no fewer) command line arguments, as follows:
First, there are one to five arguments, each argument specifying a variable among Burglary, Earthquake,
Alarm, JohnCalls, and MaryCalls and a value equal to true or false. Each of these arguments is a string
with two letters. The first letter is B (for Burglary), E (for Earthquake), A (for Alarm), J (for JohnCalls) or
M (for MaryCalls). The second letter is t (for true) or f (for false). These arguments specify a combination
C1 of events whose probability we want to compute. For example, in the first example above, C1 =
(Burglary=true and Alarm=false), and in the second example above C1 = (Alarm=false and
Earthquake=true).
Then, optionally, the word “given” follows, followed by one to four arguments. Each of these one to four
arguments is again a string with two letters, where, as before the first letter is B (for Burglary), E (for
Earthquake), A (for Alarm), J (for JohnCalls) or M (for MaryCalls). The second letter is t (for true) or f
(for false). These last arguments specify a combination of events C2 such that we need to compute the
probability of C1 given C2. For example, in the first example above C2 = (MaryCalls=false), and in the
second example there is no C2, so we simply compute the probability of C1, i.e., P(Alarm=false and
Earthquake=true).
The implementation should not contain hardcoded values for all combinations of arguments. Instead, your code
should use the tables shown on Figure 1 and the appropriate formulas to evaluate the probability of the specified
event. It is OK to hardcode values from the tables on Figure 1 in your code, but it is not OK to hard code values
for all possible command arguments, or probability values for all possible atomic events. More specifically, for
full credit, the code should include and use a Bayesian network class. The class should include a member
function called computeProbability(b, e, a, j, m), where each argument is a boolean, specifying if the
corresponding event (burglary, earthquake, alarm, john-calls, mary-calls) is true or false. This function should
return the joint probability of the five events.
Other Instructions
Also include a readme.txt file that has:
Name and UTA ID of the student.
What programming language is used.
How the code is structured.
Any specific instructions to compile and run the program.
Zip the files of your submission into optassignment2_.zip and submit the .zip file.
