bayesian decision theory (sections 2.1-2.2)

Bayesian Decision Theory

(Sections 2.1-2.2)

• Decision problem posed in probabilistic terms

• Bayesian Decision Theory–Continuous Features

• All the relevant probability values are known

Probability Density

Jain CSE 802, Spring 2013

Course OutlineMODEL INFORMATION

COMPLETE INCOMPLETE

Supervised Learning

Unsupervised Learning

Nonparametric Approach

Parametric Approach

Nonparametric Approach

Parametric Approach

Bayes Decision Theory

“Optimal” Rules

Plug-in Rules

Density Estimation

Geometric Rules (K-NN, MLP)

Mixture Resolving

Cluster Analysis (Hard, Fuzzy)

Introduction

• From sea bass vs. salmon example to “abstract” decision making problem

• State of nature; a priori (prior) probability• State of nature (which type of fish will be observed next) is

unpredictable, so it 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)

• Prior prob. reflects our prior knowledge about how likely we are to observe a sea bass or salmon; these probabilities may depend on time of the year or the fishing area!

• Bayes decision rule with only the prior information

• Decide 1 if P(1) > P(2), otherwise decide 2

• Error rate = Min {P(1) , P(2)}

• Suppose now we have a measurement or feature on the state of nature - say the fish lightness value

• Use of the class-conditional probability density

• P(x | 1) and P(x | 2) describe the difference in lightness feature between populations of sea bass and salmon

Amount of overlap between the densities determines the “goodness” of feature

• Maximum likelihood decision rule

• Assign input pattern x to class 1 if

P(x | 1) > P(x | 2), otherwise 2

• How does the feature x influence our attitude (prior) concerning the true state of nature?

• Bayes decision rule

• Posteriori probability, likelihood, evidence

• P(j , x) = P(j | x)p (x) = p(x | j) P (j)

• Bayes formula

P(j | x) = {p(x | j) . P (j)} / p(x)

where

• Posterior = (Likelihood. Prior) / Evidence

• Evidence P(x) can be viewed as a scale factor that guarantees that the posterior probabilities sum to 1

• P(x | j) is called the likelihood of j with respect to x; the category j for which P(x | j) is large is more likely to be the true category

2j

1jjj )(P)|x(P)x(P

• P(1 | x) is the probability of the state of nature being 1

given that feature value x has been observed

• Decision based on the posterior probabilities is called the Optimal Bayes Decision rule

For a given observation (feature value) X:

if P(1 | x) > P(2 | x) decide 1

if P(1 | x) < P(2 | x) decide 2

To justify the above rule, calculate the probability of error:

P(error | x) = P(1 | x) if we decide 2

P(error | x) = P(2 | x) if we decide 1

• So, for a given x, we can minimize te rob. 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)]

• Thus, for each observation x, Bayes decision rule minimizes the probability of error

• Unconditional error: P(error) obtained by integration over all x w.r.t. p(x)

• Optimal Bayes decision rule

Decide 1 if P(1 | x) > P(2 | x); otherwise decide 2

• Special cases:(i) P(1) = P(2); Decide 1 if

p(x | 1) > p(x | 2), otherwise 2

(ii) p(x | 1) = p(x | 2); Decide 1 if

P(1) > P(2), otherwise 2

Bayesian Decision Theory – Continuous Features

• Generalization of the preceding formulation

• Use of more than one feature (d features)

• Use of more than two states of nature (c classes)

• Allowing other actions besides deciding on the state of nature

• Introduce a loss function which is more general than the probability of error

• Allowing actions other than classification primarily allows the possibility of rejection

• Refusing to make a decision when it is difficult to decide between two classes or in noisy cases!

• The loss function specifies the cost of each action

• Let {1, 2,…, c} be the set of c states of nature

(or “categories”)

• Let {1, 2,…, a} be the set of a possible actions

• Let (i | j) be the loss incurred for taking

action i when the true state of nature is j

• General decision rule (x) specifies which action to take for every possible

observation x

Conditional Risk

Overall risk

R = Expected value of R(i | x) w.r.t. p(x)

Minimizing R Minimize R(i | x) for i = 1,…, a

Conditional risk

cj

1jjjii )x|(P)|()x|(R

For a given x, suppose we take the action i ; if the true state is j , we will incur the loss (i | j). P(j | x) is the prob. that the true state is j But, any one of the C states is possible for given x.

Select the action i for which R(i | x) is minimum

The overall risk R is minimized and the resulting risk is called the Bayes risk; it is the best performance that can be achieved!

• Two-category classification

1 : deciding 1

2 : deciding 2

ij = (i | j)

loss incurred for deciding i when the true state of nature is j

Conditional risk:

R(1 | x) = 11P(1 | x) + 12P(2 | x)

R(2 | x) = 21P(1 | x) + 22P(2 | x)

Bayes decision rule is stated as:

if R(1 | x) < R(2 | x)

Take action 1: “decide 1”

This results in the equivalent rule:

decide 1 if:

(21- 11) P(x | 1) P(1) >

(12- 22) P(x | 2) P(2)

and decide 2 otherwise

Likelihood ratio:

The preceding rule is equivalent to the following rule:

then take action 1 (decide 1); otherwise take action 2 (decide 2)

Note that the posteriori porbabilities are scaled by the loss differences.

)(P

)(P.

)|x(P)|x(P

if1

2

1121

2212

2

1

Interpretation of the Bayes decision rule:

“If the likelihood ratio of class 1 and class 2

exceeds a threshold value (that is independent of the input pattern x), the optimal action is to decide 1”

Maximum likelihood decision rule: the threshold value is 1; 0-1 loss function and equal class prior probability

Bayesian Decision Theory(Sections 2.3-2.5)

• Minimum Error Rate Classification

• Classifiers, Discriminant Functions and Decision Surfaces

• The Normal Density

Minimum Error Rate Classification

•Actions are decisions on classesIf action i is taken and the true state of nature is j then:the decision is correct if i = j and in error if i j

•Seek a decision rule that minimizes the probability of error or the error rate

• Zero-one (0-1) loss function: no loss for correct decision and a unit loss for any error

The conditional risk can now be simplified as:

“The risk corresponding to the 0-1 loss function is the average probability of error”

c,...,1j,i ji 1

ji 0),( ji

1jij

cj

1jjjii

)x|(P1)x|(P

)x|(P)|()x|(R

•Minimizing the risk requires maximizing the posterior probability P(i | x) since

R(i | x) = 1 – P(i | x))

•For Minimum error rate

•Decide i if P (i | x) > P(j | x) j i

• Decision boundaries and decision regions

• If is the 0-1 loss function then the threshold involves only the priors:

b1

2

a1

2

)(P

)(P2 then

0 1

2 0 if

)(P

)(P then

0 1

1 0

)|x(P

)|x(P :if decide then

)(P

)(P. Let

2

11

1

2

1121

2212

Classifiers, Discriminant Functionsand Decision Surfaces

•Many different ways to represent pattern classifiers; one of the most useful is in terms of discriminant functions

•The multi-category case

•Set of discriminant functions gi(x), i = 1,…,c

•Classifier assigns a feature vector x to class i if:

gi(x) > gj(x) j i

Network Representation of a Classifier

•Bayes classifier can be represented in this way, but the choice of discriminant function is not unique

•gi(x) = - R(i | x)

(max. discriminant corresponds to min. risk!)

•For the minimum error rate, we take gi(x) = P(i | x)

(max. discrimination corresponds to max. posterior!)

gi(x) P(x | i) P(i)

gi(x) = ln P(x | i) + ln P(i)

(ln: natural logarithm!)

•Effect of any decision rule is to divide the feature space into c decision regions

if gi(x) > gj(x) j i then x is in Ri

(Region Ri means assign x to i)

•The two-category case•Here a classifier is a “dichotomizer” that has two

discriminant functions g1 and g2

Let g(x) g1(x) – g2(x)

Decide 1 if g(x) > 0 ; Otherwise decide 2

• So, a “dichotomizer” computes a single discriminant function g(x) and classifies x according to whether g(x) is positive or not.

• Computation of g(x) = g1(x) – g2(x)

)(P

)(Pln

)|x(P

)|x(Pln

)x|(P)x|(P)x(g

2

1

2

1

21

The Normal Density

• Univariate density: N( , 2)

• Normal density is analytically tractable

• Continuous density

• A number of processes are asymptotically Gaussian

• Patterns (e.g., handwritten characters, speech signals ) can be viewed as randomly corrupted versions of a single typical or prototype (Central Limit theorem)

where: = mean (or expected value) of x 2 = variance (or expected squared deviation) of x

,x

2

1exp

2

1)x(P

2

• Multivariate density: N( , )

• Multivariate normal density in d dimensions:

where:

x = (x1, x2, …, xd)t (t stands for the transpose of a vector)

= (1, 2, …, d)t mean vector = d*d covariance matrix

|| and -1 are determinant and inverse of , respectively

• The covariance matrix is always symmetric and positive semidefinite; we assume is positive definite so the determinant of is strictly positive

• Multivariate normal density is completely specified by [d + d(d+1)/2] parameters

• If variables x1 and x2 are statistically independent then the covariance of x1 and x2 is zero.

)x()x(

2

1exp

)2(

1)x(P 1t

2/12/d

Multivariate Normal density

2 1( ) ( )tr x x

Samples drawn from a normal population tend to fall in a single cloud or cluster; cluster center is determined by the mean vector and shape by the covariance matrix

The loci of points of constant density are hyperellipsoids whose principal axes are the eigenvectors of

Transformation of Normal VariablesLinear combinations of jointly normally distributed random variables are normally distributed

Coordinate transformation can convert an arbitrary multivariate normal distribution into a spherical one

Bayesian Decision Theory (Sections 2-6 to 2-9)

• Discriminant Functions for the Normal Density

• Bayes Decision Theory – Discrete Features

Discriminant Functions for the Normal Density

• The minimum error-rate classification can be achieved by the discriminant function

gi(x) = ln P(x | i) + ln P(i)

• In case of multivariate normal densities

)(Plnln2

12ln

2

d)x()x(

2

1)x(g ii

1

ii

tii

• Case i = 2.I (I is the identity matrix)

Features are statistically independent and each feature has

the same variance

)category! ththe for thresholdthe called is (

)(Pln2

1w ;w

:where

function) ntdiscrimina (linear wxw)x(g

0i

iiti20i2

ii

0itii

i

• A classifier that uses linear discriminant functions is called “a linear machine”

• The decision surfaces for a linear machine are pieces of hyperplanes defined by the linear equations:

gi(x) = gj(x)

• The hyperplane separating Ri and Rj

is orthogonal to the line linking the means!

)()(P

)(Pln)(

2

1x ji

j

i2

ji

2

ji0

)(2

1x then )(P)(P if ji0ji

• Case 2: i = (covariance matrices of all classes are identical but otherwise arbitrary!)

• Hyperplane separating Ri and Rj

• The hyperplane separating Ri and Rj is generally not orthogonal to the line between the means!

• To classify a feature vector x, measure the squared Mahalanobis distance from x to each of the c means; assign x to the category of the nearest mean

).(

)()(

)(P/)(Pln)(

2

1x ji

ji1t

ji

jiji0

Discriminant Functions for 1D Gaussian

• Case 3: i = arbitrary

• The covariance matrices are different for each category

In the 2-category case, the decision surfaces are hyperquadrics that can assume any of the general forms: hyperplanes, pairs of hyperplanes, hyperspheres, hyperellipsoids, hyperparaboloids, hyperhyperboloids)

)(Plnln2

1

2

1 w

w

2

1W

:where

wxwxWx)x(g

iii1

iti0i

i1

ii

1ii

0itii

ti

Discriminant Functions for the Normal Density

Discriminant Functions for the Normal Density

Discriminant Functions for the Normal Density

Decision Regions for Two-Dimensional Gaussian Data

2112 1875.0125.1514.3 xxx

Error Probabilities and Integrals• 2-class problem

• There are two types of errors

• Multi-class problem – Simpler to computer the prob. of being correct (more

ways to be wrong than to be right)

Error Probabilities and Integrals

Bayes optimal decision boundary in 1-D case

Error Bounds for Normal Densities

• The exact calculation of the error for the general Guassian case (case 3) is extremely difficult

• However, in the 2-category case the general error can be approximated analytically to give us an upper bound on the error

Error Rate of Linear Discriminant Function (LDF)

• Assume a 2-class problem

• Due to the symmetry of the problem (identical ), the two types of errors are identical

• Decide if or

or

1x 1 2( ) ( )g x g x

1 11 21 1 2 2

1 1( ) ( ) log ( ) ( ) ( ) log ( )

2 2t tx x P x x P

1 1 11 22 1 1 1 2 2

1( ) log ( ) / ( )

2t tt x P P

1 1 2 2

1

~ ( , ), ~ ( , )

1( ) log ( ) ( ) ( ) log ( )

2t

i i ii i

p x N p x N

g x P x x x P

• Let• Compute expected values & variances of when

where = squared Mahalanobis distance

between

1 1 1

2 1 1 1 2 2

1( ) ( )

2t tth x x

( )h x

1 2&x x

1 1 11 1 12 1 1 1 2 2

1

2 1 2 1

1( ) ( )

21

( ) ( )2

t tt

t

E h x x E x

1

1

2 1 2 1( ) ( )t

1 2&

Error Rate of LDF

• Similarly

12 2 1 2 1

1( ) ( )

2t

1

2

( ) ~ ( , 2 )

( ) ~ ( , 2 )

p h x x N

p h x x N

22 11 1 1 12 1 1

1

2 1 2 1

( ) ( ) ( )

( ) ( )

2

t

t

E h x E x x

22 2

Error Rate of LDF

2

1( )

21 1 2 1 1

1

2

2

1( ) ( ) ( ) ( ) ~

2 2

1

2

1 1

2 2 4

t

n t

P g x g x x P h x dh h x e

e d

terf

Error Rate of LDF

2

1

2

0

2

1 1 2 2

( )log

( )

2( )

1 1

2 2 4

Total probability of error

( ) ( )

rx

e

Pt

P

erf r e dx

terf

P P P

Error Rate of LDF

1 2

1

1 2 1 21 2

10

2

( ) ( )1 1 1 1

2 2 2 24 2 2

t

P P t

erf erf

Error Rate of LDF

1

1 2 1 2

1 2

1

1 2 1 2

1 2

Mahalanobis distance is a good measure of separation between classes

(i) No Class Separation

( ) ( ) 0

1

2

(ii) Perfect Class Separation

( ) ( ) 0

0 ( 1)

t

t

erf

∞

Chernoff Bound• To derive a bound for the error, we need the following inequality

Assume conditional prob. are normal

where

Chernoff BoundChernoff bound for P(error) is found by determining the value of that minimizes exp(-k())

Error Bounds for Normal Densities• Bhattacharyya Bound

• Assume = 1/2 • computationally simpler

• slightly less tight bound

• Now, Eq. (73) has the form

When the two covariance matrices are equal, k(1/2) is te same as the Mahalanobis distance between the two means

Error Bounds for Gaussian Distributions

Chernoff Bound

Bhattacharya Bound (β=1/2)2–category, 2D data

True error using numerical integration = 0.0021

Best Chernoff error bound is 0.008190

Bhattacharya error bound is 0.008191

1 11 1 1 2( ) ( ) ( ) ( | ) ( | ) 0 1P error P P p x p x dx

1 ( )1 2( | ) ( | ) kp x p x dx e

1 1 212 1 1 2 2 1

1 2

(1 )(1 ) 1( ) ( ) [ (1 ) ] ( ) ln

2 2 | | | |

tk

(1/2)1 2 1 2 1 2( ) ( ) ( ) ( | ) ( | ) ( ) ( ) kP error P P P x P x dx P P e

1 211 2

2 1 2 11 2

1 2(1 / 2) 1 / 8( ) ( ) ln

2 2 | || |

tk

Neyman-Pearson Rule

“Classification, Estimation and Pattern recognition” by Young and Calvert

Neyman-Pearson Rule

Neyman-Pearson Rule

Neyman-Pearson Rule

Neyman-Pearson Rule

Neyman-Pearson Rule

We are interested in detecting a single weak pulse, e.g. radar reflection; the internal signal (x) in detector has mean m1 (m2) when pulse is absent (present)

Signal Detection Theory

Discriminability: ease of determining whether the pulse is present or not

The detector uses a threshold x* to determine the presence of pulse

2( * | ) :P x x x hit1( * | ) :P x x x false alarm2( * | ) :P x x x miss1( * | ) :P x x x correct rejection

For given threshold, define hit, false alarm, miss and correct rejection

21 1( | ) ~ ( , )p x N

22 2( | ) ~ ( , )p x N

1 2| |'d

Receiver Operating Characteristic (ROC)

• Experimentally compute hit and false alarm rates for fixed x*

• Changing x* will change the hit and false alarm rates

• A plot of hit and false alarm rates is called the ROC curve

Performance shown at different operating points

Operating Characteristic• In practice, distributions may not be Gaussian and

will be multidimensional; ROC curve can still be plotted

• Vary a single control parameter for the decision rule and plot the resulting hit and false alarm rates

Bayes Decision Theory – Discrete Features

• Components of x are binary or integer valued; x can take only one of m discrete values

v1, v2, …,vm

• Case of independent binary features for 2-category problem

Let x = [x1, x2, …, xd ]t where each xi is either 0 or 1, with probabilities:

pi = P(xi = 1 | 1)

qi = P(xi = 1 | 2)

• The discriminant function in this case is:

0g(x) if and0 g(x) if decide

)(P

)(Pln

q1

p1lnw

:and

d,...,1i )p1(q

)q1(plnw

:where

wxw)x(g

21

2

1d

1i i

i0

ii

iii

0i

d

1ii

Bayesian Decision for Three-dimensional Binary Data

Decision boundary for 3D binary features. Left figure shows the case when pi=.8 and qi=.5. Right figure shows case when p3=q3 (Feature 3 is not providing any discriminatory information) so decision surface is parallel to x3 axis

• Consider a 2-class problem with three independent binary features; class priors are equal and pi = 0.8 and qi = 0.5, i = 1,2,3• wi = 1.3863• w0 = 1.2• Decision surface g(x) = 0 is shown below

Handling Missing Features

• Suppose it is not possible to measure a certain feature for a given pattern

• Possible solutions:

• Reject the pattern

• Approximate the missing feature

• Mean of all the available values for the missing feature

• Marginalize over the distribution of the missing feature

Handling Missing Features

Other Topics• Compound Bayes Decision Theory & Context

– Consecutive states of nature might not be statistically independent; in sorting two types of fish, arrival of next fish may not be independent of the previous fish

– Can we exploit such statistical dependence to gain improved performance (use of context)

– Compound decision vs. sequential compound decision problems

– Markov dependence

• Sequential Decision Making

– Feature measurement process is sequential (as in medical diagnosis)

– Feature measurement cost

– Minimize the no. of features to be measured while achieving a sufficient accuracy; minimize a combination of feature measurement cost & classification accuracy

Context in Text Recognition