5. Consider the following decision rule for a two-category decision problem where the feature is a scalar r: Given a contant c, decide w, is the true state of nature if r > e, otherwise decide wz is the true state of nature. (a) Show that the probability of error for this rule is given by PR = P(ur) (rlwi)dr + P(w2) S(r w)dr (b) By differentiating the expression above, show that a necessary condition to min- imize Pr is that e satisfy S(clun )P(w) = S(clws)P(w) What optimality criterion does this condition imply? (ie. Bayes optimal, Maxi- mum Likelihood, etc.)

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5. Consider the following decision rule for a two-category decision problem where the
feature is a scalar r: Given a contant c, decide w, is the true state of nature if r > c,
otherwise decide wr is the true state of nature.
(a) Show that the probability of error for this rule is given by
PE = P(wr) (2 lwi)dr + P(w2) S(x \w)dr
(b) By differentiating the expression above, show that a necessary condition to min-
imize Pr is that c satisfy
S(clwr)P(w) = f(clws)P(w2)
What optimality criterion does this condition imply? (ie. Bayes optimal, Maxi-
mum Likelihood, etc.)
Transcribed Image Text:5. Consider the following decision rule for a two-category decision problem where the feature is a scalar r: Given a contant c, decide w, is the true state of nature if r > c, otherwise decide wr is the true state of nature. (a) Show that the probability of error for this rule is given by PE = P(wr) (2 lwi)dr + P(w2) S(x \w)dr (b) By differentiating the expression above, show that a necessary condition to min- imize Pr is that c satisfy S(clwr)P(w) = f(clws)P(w2) What optimality criterion does this condition imply? (ie. Bayes optimal, Maxi- mum Likelihood, etc.)
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