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Bayesian Decision Theory – Continuous Features

Bayesian Decision Theory – Continuous Features. Team teaching. Introduction. The sea bass/salmon example State of nature, prior State of nature is a random variable The catch of salmon and sea bass is equiprobable P(  1 ) = P(  2 ) (uniform priors)

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Bayesian Decision Theory – Continuous Features

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  1. Bayesian Decision Theory– Continuous Features Team teaching

  2. Introduction • The sea bass/salmon example • State of nature, prior • State of nature is a random variable • The catch of salmon and sea bass is equiprobable • P(1) = P(2) (uniform priors) • P(1) + P( 2) = 1 (exclusivity and exhaustivity) Pattern Classification, Chapter 2 (Part 1)

  3. Decision rule with only the prior information • Decide 1 if P(1) > P(2) otherwise decide 2 • Use of the class –conditional information • P(x | 1) and P(x | 2) describe the difference in lightness between populations of sea and salmon Pattern Classification, Chapter 2 (Part 1)

  4. Pattern Classification, Chapter 2 (Part 1)

  5. Posterior, likelihood, evidence • P(j | x) = P(x | j) . P (j) / P(x) • Where in case of two categories • Posterior = (Likelihood. Prior) / Evidence Pattern Classification, Chapter 2 (Part 1)

  6. Pattern Classification, Chapter 2 (Part 1)

  7. Decision given the posterior probabilities X is an observation for which: if P(1 | x) > P(2 | x) True state of nature = 1 if P(1 | x) < P(2 | x) True state of nature = 2 Therefore: whenever we observe a particular x, the probability of error is : P(error | x) = P(1 | x) if we decide 2 P(error | x) = P(2 | x) if we decide 1 Pattern Classification, Chapter 2 (Part 1)

  8. Minimizing the probability of error • Decide 1 if P(1 | x) > P(2 | x);otherwise decide 2 Therefore: P(error | x) = min [P(1 | x), P(2 | x)] (Bayes decision) Pattern Classification, Chapter 2 (Part 1)

  9. Bayesian Decision Theory – Continuous Features • Generalization of the preceding ideas • Use of more than one feature • Use more than two states of nature • Allowing actions and not only decide on the state of nature • Introduce a loss of function which is more general than the probability of error Pattern Classification, Chapter 2 (Part 1)

  10. Allowing actions other than classification primarily allows the possibility of rejection • Refusing to make a decision in close or bad cases! • The loss function states how costly each action taken is Pattern Classification, Chapter 2 (Part 1)

  11. Let {1, 2,…, c} be the set of c states of nature (or “categories”) Let {1, 2,…, a}be the set of possible actions Let (i | j)be the loss incurred for taking action i when the state of nature is j Pattern Classification, Chapter 2 (Part 1)

  12. Overall risk R = Sum of all R(i | x) for i = 1,…,a Minimizing R Minimizing R(i | x) for i = 1,…, a for i = 1,…,a Conditional risk Pattern Classification, Chapter 2 (Part 1)

  13. Select the action i for which R(i | x) is minimum R is minimum and R in this case is called the Bayes risk = best performance that can be achieved! Pattern Classification, Chapter 2 (Part 1)

  14. Diagram of pattern classification Procedure of pattern recognition and decision making Features x Inner belief w Observables X Action a subjects • X--- all the observables using existing sensors and instruments • x --- is a set of features selected from components of X, or linear/non-linear functions of X. • w --- is our inner belief/perception about the subject class. • --- is the action that we take for x. We denote the three spaces by

  15. Examples Ex 1: Fish classification X=I is the image of fish, x =(brightness, length, fin#, ….) w is our belief what the fish type is Wc={“sea bass”, “salmon”, “trout”, …} a is a decision for the fish type, in this case Wc= Wa Wa ={“sea bass”, “salmon”, “trout”, …} Ex 2: Medical diagnosis X= all the available medical tests, imaging scans that a doctor can order for a patient x =(blood pressure, glucose level, cough, x-ray….) w is an illness type Wc={“Flu”, “cold”, “TB”, “pneumonia”, “lung cancer”…} a is a decision for treatment, Wa ={“Tylenol”, “Hospitalize”, …}

  16. Tasks Features x Inner belief w Observables X Decision a subjects statistical inference control sensors selecting Informative features risk/cost minimization In Bayesian decision theory, we are concerned with the last three steps in the big ellipse assuming that the observables are given and features are selected.

  17. Bayes Decision • It is the decision making when all underlying probability distributions are known. • It is optimal given the distributions are known. • For two classes w1 and w2 , • Prior probabilities for an unknown new observation: • P(w1) : the new observation belongs to class 1 • P(w2) : the new observation belongs to class 2 • P(w1 ) + P(w2 ) = 1 • It reflects our prior knowledge. It is our decision rule when no feature on the new object is available: • Classify as class 1 if P(w1 ) > P(w2 )

  18. Bayesian Decision Theory statistical Inference risk/cost minimization Inner belief p(w|x) Features x Decision a(x) Two probability tables: a). Prior p(w) b). Likelihood p(x|w) A risk/cost function (is a two-way table) l(a | w) The belief on the class w is computed by the Bayes rule The risk is computed by

  19. Bayes Decision We observe features on each object. P(x| w1) & P(x| w2) : class-specific density The Bayes rule:

  20. Decision Rule A decision rule is a mapping function from feature space to the set of actions we will show that randomized decisions won’t be optimal. A decision is made to minimize the average cost / risk, It is minimized when our decision is made to minimize the cost / risk for each instance x.

  21. Bayesian error In a special case, like fish classification, the action is classification, we assume a 0/1 error. The risk for classifying x to class ai is, The optimal decision is to choose the class that has maximum posterior probability The total risk for a decision rule, in this case, is called the Bayesian error

  22. An example of fish classification

  23. 3. It is known that 1% of population suffers from a particular disease. A blood test has a 97% chance to identify the disease for a diseased individual, by also has a 6% chance of falsely indicating that a healthy person has a disease. a. What is the probability that a random person has a positive blood test. b. If a blood test is positive, what’s the probability that the person has the disease? c. If a blood test is negative, what’s the probability that the person does not have the disease? Example

  24. S is a boolean RV indicating whether a person has a disease. P(S) = 0.01; P(S’) = 0.99. • T is a boolean RV indicating the test result ( T = true indicates that test is positive.) • P(T|S) = 0.97; P(T’|S) = 0.03; • P(T|S’) = 0.06; P(T’|S’) = 0.94; • (a) P(T) = P(S) P(T|S) + P(S’)P(T|S’) = 0.01*0.97 +0.99 * 0.06 = 0.0691 • (b) P(S|T)=P(T|S)*P(S)/P(T) = 0.97* 0.01/0.0691 = 0.1403 • (c) P(S’|T’) = P(T’|S’)P(S’)/P(T’)= P(T’|S’)P(S’)/(1-P(T))= 0.94*0.99/(1-.0691)=0.9997

  25. A physician can do two possible actions after seeing patient’s test results: • A1 - Decide the patient is sick • A2 - Decide the patient is healthy • The costs of those actions are: • If the patient is healthy, but the doctor decides he/she is sick - $20,000. • If the patient is sick, but the doctor decides he/she is healthy - $100.000 • When the test is positive: • R(A1|T) = R(A1|S)P(S|T) + R(A1|S’) P(S’|T) = R(A1|S’) *P(S’|T) = 20.000* P(S’|T) = 20.000*0.8597 = $17194.00 • R(A2|T) = R(A2|S)P(S|T) + R(A2|S’) P(S’|T) = R(A2|S)P(S|T) = 100000* 0.1403 = $14030.00

  26. A physician can do three possible actions after seeing patient’s test results: • Decide the patient is sick • Decide the patient is healthy • Send the patient for another test • The costs of those actions are: • If the patient is healthy, but the doctor decides he/she is sick - $20,000. • If the patient is sick, but the doctor decides he/she is healthy - $100.000 • Sending the patient for another test costs $15,000

  27. When the test is positive: • R(A1|T) = R(A1|S)P(S|T) + R(A1|S’) P(S’|T) = R(A1|S’) *P(S’|T) = 20.000* P(S’|T) = 20.000*0.8597 = $17194.00 • R(A2|T) = R(A2|S)P(S|T) + R(A2|S’) P(S’|T) = R(A2|S)P(S|T) = 100000* 0.1403 = $14030.00 • R(A3|T) = $15000.00 • When the test is negative: • R(A1|T’) = R(A1|S)P(S|T’) + R(A1|S’) P(S’|T’) = R(A1|S’) P(S’|T’) = 20,000* 0.9997 = $19994.00 • R(A2|T’) = R(A2|S)P(S|T’) + R(A2|S’) P(S’|T’) = R(A1|S) P(S|T’)= 100,000*0.0003 = $30.00 • R(A3|T’) = 15000.00

  28. Excercise • Consider the example of sea bass – salmon classifier, let these two possible actions: A1: Decide the input is sea bass; A2: Decide the input is salmon. Prior for sea bass and salmon are 2/3 and 1/3, respectively. • The cost of classifying a fish as a salmon when it truly is sea bass is 2$, and The cost of classifying a fish as a sea bass when it is truly a salmon is 1$. • Find the decision for input X = 13, whereas the likelihood P(X|ω1) = 0.28, and P(X|ω2) = 0.17

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