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    01 Probability review 01 Probability review Presentation Transcript

    • Bayesian Networks Unit 1 Probability Review Wang, Yuan-Kai, 王元凱 ykwang@mails.fju.edu.tw http://www.ykwang.tw Department of Electrical Engineering, Fu Jen Univ. 輔仁大學電機工程系 2006~2011 Reference this document as: Wang, Yuan-Kai, “Probability Review," Lecture Notes of Wang, Yuan-Kai, Fu Jen University, Taiwan, 2008.Fu Jen University Department of Electronic Engineering Yuan-Kai Wang Copyright
    • 王元凱 Unit - Probability Review p. 2 Goal of this Unit  Review basic concepts of probability in terms of  Image processing terminologiesFu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright
    • 王元凱 Unit - Probability Review p. 3 Related Units  Next units  Statistics Review  Uncertainty Inference (Discrete)  Uncertainty Inference (Continuous)Fu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright
    • 王元凱 Unit - Probability Review p. 4 Self-Study  Probability Theory textbook  Artificial Intelligence: a modern approach  Russell & Norvig, 2nd, Prentice Hall, 2003. pp.462~474,  Chapter 13, Sec. 13.1~13.3Fu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright
    • 王元凱 Unit - Probability Review p. 5 Contents 1. Probability ......................................... 6 2. Random Variable .............................. 9 3. Probability Distribution .................... 19 4. Joint Distribution .............................. 26 5. Conditional Probability .................... 46 6. Bayes Theorem ................................. 59 7. Summary …………………………….. 64 8. References ………………………….. 67Fu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright
    • 王元凱 Unit - Probability Review p. 6 1. Probability  We write P(A) as “the fraction of possible worlds in which A is true” Event space of all possible worlds Worlds in P(A) = Area of which A is true reddish oval Its area is 1 Worlds in which A is FalseFu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright
    • 王元凱 Unit - Probability Review p. 7 The Axioms of Probability 0  P(A)  1  P(True) = 1  P(False) = 0  P(A  B) = P(A) + P(B) - P(A  B) Where do these axioms come from? Were they “discovered”? Answers coming up later.Fu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright
    • 王元凱 Unit - Probability Review p. 8 Theorems from the Axioms 0  P(A)  1, P(True) = 1, P(False) = 0 P(A  B) = P(A) + P(B) - P(A  B) From these we can prove: P(not A) = P(~A) = 1-P(A) P(A) = P(A  B) + P(A  ~B) How?Fu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright
    • 王元凱 Unit - Probability Review p. 9 2. Random Variable A variable with randomness probability, degree of belief An example Rain : a random variable Its possible values: true, false Its randomness P(Rain=true)=0.8, P(Rain=false)=0.2Fu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright
    • 王元凱 Unit - Probability Review p. 10 Types of Random Variable Boolean random variable Rain : true, false Discrete random variable (Multivalued R.V.) Rain: cloudy, sunny, drizzle, drench Continuous random variable Rain: rainfall in millimeterFu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright
    • 王元凱 Unit - Probability Review p. 11 Discrete R.V. (1/4) Let A=Rain A can take on more than 2 values A is a random variable with arity k if it can take on exactly one value out of {v1,v2, .. vk} Thus P ( A  vi  A  v j )  0 if i  j P ( A  v1  A  v2    A  vk )  1Fu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright
    • 王元凱 Unit - Probability Review p. 12 Discrete R.V. (2/4) i P( A  v1  A  v2    A  vi )   P( A  v j ) j 1 Prove it? Using the axioms of probability… 0  P(A)  1, P(True) = 1, P(False) = 0 P(A  B) = P(A) + P(B) - P(A  B)  And assume that A obeys P ( A  vi  A  v j )  0 if i  j P( A  v1  A  v2    A  vk )  1Fu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright
    • 王元凱 Unit - Probability Review p. 13 Discrete R.V. (3/4) i P( A  v1  A  v2    A  vi )   P( A  v j ) k j 1  P( A  v )  1 j 1 j Using the axioms of probability… 0  P(A)  1, P(True) = 1, P(False) = 0 P(A or B) = P(A) + P(B) - P(A and B)  And assuming that A obeys… P( A  vi  A  v j )  0 if i  j P( A  v1  A  v2    A  vk )  1Fu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright
    • 王元凱 Unit - Probability Review p. 14 Discrete R.V. (4/4) i P( B  [ A  v1  A  v2    A  vi ])   P( B  A  v j ) j 1 k P( B)   P( B  A  v j ) j 1 Using the axioms of probability… 0  P(A)  1, P(True) = 1, P(False) = 0 P(A or B) = P(A) + P(B) - P(A and B)  And assuming that A obeys… P( A  vi  A  v j )  0 if i  j P( A  v1  A  v2    A  vk )  1Fu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright
    • 王元凱 Unit - Probability Review p. 15 Random Vector  A random vector is a vector of random variables  X = (X1, X2, ..., XN)  X1 ... XN are random variablesFu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright
    • 王元凱 Unit - Probability Review p. 16 Example in Image Processing  For a gray-level image, random variable is “gray level” • Random variable X (Gray level) has n possible values {x1, x2, ..., xn}, n=256 • N random data x1, x2, .., xN of X, N=Width*Height Discrete v.s. continuous ?Fu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright
    • 王元凱 Unit - Probability Review p. 17 Example in Image Processing  For a color image, random vector is (r,g,b)Fu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright
    • 王元凱 Unit - Probability Review p. 18 Example in Computer Vision  Random vector is a vector of “features”  Face recognitionFu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright
    • 王元凱 Unit - Probability Review p. 19 3. Probability Distribution  Probability distribution is a set of probabilities  Boolean R.V.  P(A): P(A=true)=0.2, P(A=false)=0.8  Discrete R.V.  P(A): P(A=v1)=0.2, P(A=v2)=0.4, P(A=v3)=0.4, P(A=v4)=0.1,  Continuous R.V.  Usually we plot the probability distribution as a function (Probability Distribution Function, pdf)Fu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright
    • 王元凱 Unit - Probability Review p. 20 Probability Distribution (1/3) Continuous R.V. 0.8 0.6 0.4 0.2 0 Rain (mm) 0 100 200 300 400 500 600 • P(Rain) is a probability density function (pdf) • P(Rain=200) is a probabilityFu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright
    • 王元凱 Unit - Probability Review p. 21 Probability Distribution(2/3) Boolean R.V. P(Rain) is a probability distribution P(Rain=false) is a probability P(Rain) 0.8 0.2 Rain false trueFu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright
    • 王元凱 Unit - Probability Review p. 22 Probability Distribution(3/3) Discrete R.V. P(Rain) is a probability distribution P(Rain=drizzle) is a probability P(Rain) 0.8 0.2 Rain drench cloudy drizzle sunnyFu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright
    • 王元凱 Unit - Probability Review p. 23 Common Probability Distributions Normal distribution (Gaussian) Parameters: ,  Discussed in Section 3 Uniform distribution Exponential distribution Poisson distribution ...Fu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright
    • 王元凱 Unit - Probability Review p. 24 The Uniform Distribution 1 w  w if | x | P(x) p( x)   2 w  0 if | x |  2 1/w X -w/2 0 w/2 2 w E[ X ]  0 Var[ X ]  12Fu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright
    • 王元凱 Unit - Probability Review p. 25 Example in Image Processing • Random variable X (Gray level) has n possible values {x1, x2, ..., xn}, n=256 • Distribution P(xi) of the image •Is not a usual p.d.f.Fu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright
    • 王元凱 Unit - Probability Review p. 26 4. Joint Distribution  When there are more than one random variables: X, Y  We want to know "the probabilities of both random variables"  Joint probability distribution  P(X  Y)  P(X  Y  ...)Fu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright
    • 王元凱 Unit - Probability Review p. 27 An Example A set of random variables for the typhoon world  Rain, Wind, Speed, ...  Every random variable Describes one facet of typhoon Has a probability distribution  A typhoon is a situation of joint probability P(Rain=200mm  Wind=South  Speed=100km/hr) = 0.4Fu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright
    • 王元凱 Unit - Probability Review p. 28 P(X,Y) with X Y are Discrete  P(X,Y) : (M x N) entries y1 P(X=x1,Y=y1) P(X=x2,Y=y1) P(X=x3,Y=y1) Y y2 P(X=x1,Y=y2) P(X=x2,Y=y2) P(X=x3,Y=y2) x1 x2 x3 X y1 0.2 0.1 0.1 Y y2 0.1 0.2 0.3 x1 x2 x3 XFu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright
    • 王元凱 Unit - Probability Review p. 29 Atomic Event Atomic event is a Combination of values of all random variables Situation (state) of typhoon (statistical world) For the typhoon example (Rain=200mm  Wind=South  Speed=100km/hr) is an atomic event (Rain=200mm  Wind=South) is notFu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright
    • 王元凱 Unit - Probability Review p. 30 Joint Probability Distribution  P(Rain=200mm  Wind=South  Speed=100km/hr) is a joint probability  P(Rain  Wind  Speed) is a probability of all joint probabilities  Joint probability function A set of random variables can have mixed types  Ex: Rain: Boolean, Wind: Discrete, Speed: Continuous  We will consider all-discrete caseFu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright
    • 王元凱 Unit - Probability Review p. 31 Prior and Joint Probability (Math) Let {X1,...,Xn} be a set of random variables P(Xi) is a probability function P(Xi=xi) is a prior probability P(X1  X2    Xn) is a joint probability function P(X1=x1  X2=x2    Xn=xn) is a joint probabilityFu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright
    • 王元凱 Unit - Probability Review p. 32 For Boolean & Discrete R.V. Let X be a single R.V. P(X) is a vector Boolean R.V. Rain: true, false P(Rain) = <0.72, 0.28> Discrete R.V. Rain: cloudy, sunny, drizzle, drench P(Rain) = <0.72, 0.1, 0.08, 0.1> Normalized, i.e. sums to 1Fu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright
    • 王元凱 Unit - Probability Review p. 33 Full Joint Distribution (1/3) For a set of random variables { X1 , X2 ,  , Xn } X1  X2    Xn are atomic events P(X1  X2    Xn) is A full joint probability distribution (FJD) A table of all joint prob. of all atomic events, if {X1,  , Xn} are discreteFu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright
    • 王元凱 Unit - Probability Review p. 34 Full Joint Distribution (2/3) Ex: X1: Rain, X2: Wind X1: drizzle, drench, cloudy, X2: strong, weak X1  X2 are atomic events P(X1  X2) is a 3x2 matrix of values Rain Drizzle Drench Cloudy Wind Strong 0.15 0.12 0.06 Weak 0.55 0.08 0.04 P(X1=drizzle  X2=strong) = 0.15, ...Fu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright
    • 王元凱 Unit - Probability Review p. 35 Full Joint Distribution (3/3) All questions about probability of joint events can be answered by the table P(Wind=Strong), P(Rain=Drizzle  Wind=Strong), ...Fu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright
    • 王元凱 Unit - Probability Review p. 36 An Example with 3 Boolean R.V. (1/2) Boolean variables A, B, C 1. Make a truth table listing all A B C combinations of 0 0 0 values of your 0 0 1 variables 0 1 0 • if there are M 0 1 1 Boolean variables 1 0 0 then the table will 1 0 1 1 1 0 have 2M rows 1 1 1Fu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright
    • 王元凱 Unit - Probability Review p. 37 An Example with 3 Boolean R.V. (2/2) 2. For each A B C Prob combination of 0 0 0 0.30 values, say how 0 0 1 0.05 probable it is 0 1 0 0.10 0 1 1 0.05 A 0.05 0.10 0.05 1 0 0 0.05 0.10 1 0 1 0.10 0.25 0.05 C 1 1 0 0.25 0.10 1 1 1 0.10 0.30 B Sum 1.0Fu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright
    • 王元凱 Unit - Probability Review p. 38 Using the FJD • Once you have the FJD • You can ask for the probability of any logical expression: Inference P( E )   P(row ) rows matching EFu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright
    • 王元凱 Unit - Probability Review p. 39 Examples of Inference (1/3) P(Poor Male) = 0.4654 P( E )   P(row ) rows matching EFu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright
    • 王元凱 Unit - Probability Review p. 40 Examples of Inference (2/3) P(Poor) = 0.7604 P( E )   P(row ) rows matching EFu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright
    • 王元凱 Unit - Probability Review p. 41 Examples of Inference (3/3) P ( E1  E2 )  P(row ) P ( E1 | E2 )   rows matching E1 and E2 P ( E2 )  P(row ) rows matching E2 P(Male | Poor) = 0.4654 / 0.7604 = 0.612Fu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright
    • 王元凱 Unit - Probability Review p. 42 Inference is a Big Deal I’ve got this evidence What’s the chance that this conclusion is true? I’ve got a sore neck: how likely am I to have meningitis? I see my lights are out and it’s 9pm. What’s the chance my spouse is already asleep?Fu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright
    • 王元凱 Unit - Probability Review p. 43 Where do Full Joint Distributions Come from? (1/2)  Idea One: Expert Humans  Idea Two: Learn them from data!Fu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright
    • 王元凱 Unit - Probability Review p. 44 Where do FJD Come from? (2/2) Build a JD table for your Then fill in each row with attributes in which the probabilities are unspecified ˆ (row )  records matching row P total number of records A B C Prob 0 0 0 ? A B C Prob 0 0 1 ? 0 0 0 0.30 0 1 0 ? 0 0 1 0.05 0 1 1 ? 0 1 0 0.10 1 0 0 ? 0 1 1 0.05 1 0 1 ? 1 0 0 0.05 1 1 0 ? 1 0 1 0.10 1 1 1 ? 1 1 0 0.25 1 1 1 0.10 Fraction of all records in which A and B are True but C is FalseFu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright
    • 王元凱 Unit - Probability Review p. 45 Example of Learning a FJD  This Joint was obtained by learning from three attributes in the UCI “Adult” Census Database [Kohavi 1995]Fu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright
    • 王元凱 Unit - Probability Review p. 46 5. Conditional Probability  P(A|B) = Fraction of worlds in which B is true that also have A true H = “Have a headache” F F = “Coming down with Flu” H P(H) = 1/10 P(F) = 1/40 P(H|F) = 1/2 “Headaches are rare and flu is rarer, but if you’re coming down with ‘flu there’s a 50- 50 chance you’ll have a headache”Fu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright
    • 王元凱 Unit - Probability Review p. 47 Formula H = “Have a headache” F F = “Coming down with Flu” H P(H) = 1/10 P(F) = 1/40 P(H|F) = 1/2 P( H  F ) P( H | F )  P( F ) P(H|F) v.s. P(HF)Fu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright
    • 王元凱 Unit - Probability Review p. 48 Conditional, Joint, Prior  Relationship among conditional, joint, and prior probabilities P(X1  X2)P(X2) P(X1) P( X 1  X 2 ) P( X 1 | X 2 )  P( X 2 ) P( X1  X 2 )  P( X1 | X 2 )P( X 2 )Fu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright
    • 王元凱 Unit - Probability Review p. 49 Posterior v.s. Prior Probabilities P(Cavity|Toothache) Conditional probability Posterior probability (after the fact/evidence) P(Cavity) Prior probability (the fact)Fu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright
    • 王元凱 Unit - Probability Review p. 50 An Example (1/2) For a dental diagnosis Let {Cavity,Toothache} be a set of Boolean random variables Denotations for Boolean R.V. P(Cavity=true) = P(cavity) P(Cavity=false) = P(cavity)Fu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright
    • 王元凱 Unit - Probability Review p. 51 An Example (2/2) The full joint probability distribution P(Toothache  Cavity) toothache toothache cavity 0.04 0.06 cavity 0.01 0.89 P(cavity  toothache)  0.04  0.01  0.06  0.11 P(cavity| toothache ) P(cavity toothache ) 0.04    0.80 P(toothache ) 0.04  0.01Fu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright
    • 王元凱 Unit - Probability Review p. 52 Conditional Probability (Math) P(X1=x1i| X2 =x2j) is a conditional probability P(X1 | X2) is a conditional distribution function All P(X1=x1i| X2 =x2j) for all possible i, j For Boolean & discrete R.V.s, conditional distribution function is a conditional probability table Conditional distribution of continuous R.V. will not used in our discussionsFu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright
    • 王元凱 Unit - Probability Review p. 53 Conditional Probability Table (CPT) For the dental diagnosis problem, Toothache & Cavity are Boolean R.V.s P(Toothache|Cavity) is a CPT Cavity P(toothache|Cavity) P(toothache|Cavity) T 0.90 0.1 F 0.05 0.95 Cavity P(toothache|Cavity) T 0.90 F 0.05Fu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright
    • 王元凱 Unit - Probability Review p. 54 CPT v.s. FJD toothache toothache cavity 0.04 0.06 cavity 0.01 0.89 Sum of all atomic events = 1 Cavity P(toothache|Cavity) P(toothache|Cavity) T 0.90 0.1 F 0.05 0.95 Sum of a row = 1Fu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright
    • 王元凱 Unit - Probability Review p. 55 More Than 2 Random Variables in Conditional Probability Joint conditional probability P( X1  X 2 | X 3  X 4 ) P( X1  X 2  X 3  X 4 )  P( X 3  X 4 )  P( X1 | X 2  X 3  X 4 )P( X 2 | X 3  X 4 )Fu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright
    • 王元凱 Unit - Probability Review p. 56 Chain Rule • Joint probability can be calculated from a chain of conditional probability P( X | X )  P( X 1  X 2 ) 1 2 P( X 2 ) P( X 1  X 2 )  P( X 1 | X 2 ) P( X 2 ) P( X1  X 2  X 3  X 4 )  P( X1  ( X 2  X 3  X 4 ))  P( X1 | X 2  X 3  X 4 )P( X 2  X 3  X 4 )  P( X1 | X 2  X 3  X 4 )P( X 2 | X 3  X 4 )P( X 3 | X 4 )P( X 4 ) n P( X1  X 2  X n )   P( X i | X i 1  X n ) i 1Fu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright
    • 王元凱 Unit - Probability Review p. 57 Useful Easy-to-prove Facts P( A | B)P(A | B )  1 nA  P( A  v k 1 k | B)  1Fu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright
    • 王元凱 Unit - Probability Review p. 58 Where Are We?  We have recalled the fundamentals of probability  We know what JDs are and how to use them  Next two sections  Bayes rule  Gaussian distributionFu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright
    • 王元凱 Unit - Probability Review p. 59 6. Bayes Theorem P ( A | B ) P ( A)  P ( B | A) P ( B ) P( A  B)  P( A | B) P( B)  P ( B  A)  P ( B | A) P ( A)  P ( A | B ) P ( B )  P ( B | A) P ( A) Bayes, Thomas (1763) An essay towards solving a problem in the doctrine of chances. Philosophical Transactions of the Royal Society of London, 53:370-418Fu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright
    • 王元凱 Unit - Probability Review p. 60 Useful Forms of Bayes Rule P( B | A) P( A) P( A  B) P( A | B)   P( B) P( B) P( B | A  C ) P( A  C ) P( A |B  C )  P( B  C ) P( B | A, C ) P( A, C ) P( B | A, C ) P( A | C ) P( A |B, C )   P ( B, C ) P( B | C )Fu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright
    • 王元凱 Unit - Probability Review p. 61 More General Forms of Bayes Rule If A is a Boolean R.V. P( A  B) P( A  B) P( A |B)   P( B) P ( A  B )  P ( A  B ) If A is a discrete R.V. P( A  vi  B) P(B | A  vi )P( A  vi ) P( A  vi |B)  nA  nA P(A  v  B) P(B | A  v )P(A  v ) k 1 k k 1 k kFu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright
    • 王元凱 Unit - Probability Review p. 62 Example – Bayesian Detection (1/2)  2-class recognition  Ex.: Skin color detection  Let  A be skin color pixel  A be non-skin color pixel  c be the color (R,G,B) of a pixel  We can get : P(c|A), P(c|A)  For a pixel in a new image, is it a skin color pixel? P(c | A) P( A) P(c | A) P(A) P( A | c)  > P ( A | c )  P (c ) P (c )Fu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright
    • 王元凱 Unit - Probability Review p. 63 Example – Bayesian Detection (2/2)  2-class recognition  Ex.: Background detection  Let  A be background pixel  A be non-background pixel  c be the color (R,G,B) of a pixel  We can get : P(c|A), P(c|A)  For a pixel in a new image, is it a background pixel? P(c | A) P( A) P(c | A) P(A) P( A | c)  > P ( A | c )  P (c ) P (c )Fu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright
    • 王元凱 Unit - Probability Review p. 64 7. Summary Continuous variable Discrete variable (X has M values, Y has N values) P(X) Function of one variable M vector P(X=x) Scalar* Scalar P(X,Y) Function of two variables MxN matrix P(X|Y) Function of two variables MxN matrix P(X|Y=y) Function of one variable M vector P(X=x|Y) Function of one variable N vector P(X=x|Y=y) Scalar* ScalarFu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright
    • 王元凱 Unit - Probability Review p. 65 Ex (1/2)Fu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright
    • 王元凱 Unit - Probability Review p. 66 Ex (2/2)Fu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright
    • 王元凱 Unit - Probability Review p. 67 8. Reference  Artificial Intelligence: a modern approach  Russell & Norvig, 2nd, Prentice Hall, 2003.  Sections 13.1~13.3, pp.462~474.  統計學的世界  墨爾著,鄭惟厚譯  天下文化,2002  深入淺出統計學  D. Grifiths, 楊仁和譯,2009  O’ ReillyFu Jen University Department of Electrical Engineering Wang, Yuan-Kai Copyright