Question
The table shows the examples of SPAM and those of HAM messages which are consisted of some words whose dictionary size is twelve. Suppose that
The table shows the examples of SPAM and those of HAM messages which are consisted of some words whose dictionary size is twelve. Suppose that youve received SPAM messages for the 1st 3 days, then HAM messages for the next 5 days, i.e. a data as a sequence of message is
SPAM | HAM |
offer is secret | play golf tomorrow |
click secret link | went play golf |
secret golf link | secret golf event |
| golf is tomorrow |
| golf costs money |
(a) Compute the maximum likelihood of SPAM, i.e. P(SPAM)=q, using a log-likelihood.
(b) In the Bayesian network of this ML parameter learning,
(i) Draw the BN with the CPT of the required parameters (e.g. q1, q2, ..). -- You dont have to compute the exact values of parameters yet.
(ii) How many parameters are required?
(c) By ML-learning, compute a parameter value, P(secret | SPAM) and P(secret | HAM), respectively.
(d) Now, the new message golf is received. What is the probability that this message is SPAM?
(e) The new message secret is secret is received. What is the probability that this is SPAM?
(f) For a new message, tomorrow is secret, what is the probability that the message is SPAM and HAM, respectively?
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