# LECTURE 05: BAYESIAN ESTIMATION

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ECE 8443 – Pattern RecognitionECE 8527 – Introduction to Machine Learning and Pattern Recognition

LECTURE 05: BAYESIAN ESTIMATION

• Objectives:Bayesian EstimationExample

• Resources:D.H.S.: Chapter 3 (Part 2)J.O.S.: Bayesian Parameter EstimationA.K.: The Holy TrinityA.E.: Bayesian MethodsJ.H.: Euro Coin

ECE 8527: Lecture 05, Slide 2

• In Chapter 2, we learned how to design an optimal classifier if we knew the prior probabilities, P(ωi), and class-conditional densities, p(x|ωi).

• Bayes: treat the parameters as random variables having some known prior distribution. Observations of samples converts this to a posterior.

• Bayesian learning: sharpen the a posteriori density causing it to peak near the true value.

• Supervised vs. unsupervised: do we know the class assignments of the training data.

• Bayesian estimation and ML estimation produce very similar results in many cases.

• Reduces statistical inference (prior knowledge or beliefs about the world) to probabilities.

Introduction to Bayesian Parameter Estimation

ECE 8527: Lecture 05, Slide 3

• Posterior probabilities, P(ωi|x), are central to Bayesian classification.• Bayes formula allows us to compute P(ωi|x) from the priors, P(ωi), and the

likelihood, p(x|ωi).• But what If the priors and class-conditional densities are unknown?• The answer is that we can compute the posterior, P(ωi|x), using all of the

information at our disposal (e.g., training data).• For a training set, D, Bayes formula becomes:

c

jjj

iii

DPDp

DPDpDP

1)(),(

)(),(evidence

priorlikelihood),|(

x

xx

• We assume priors are known: P(ωi|D) = P(ωi).

• Also, assume functional independence:

Di have no influence on

This gives:

c

jjjj

iiii

PDp

PDpDP

1)(),(

)(),(),|(

x

xx

Class-Conditional Densities

jiifDp j ),( x

ECE 8527: Lecture 05, Slide 4

• Assume the parametric form of the evidence, p(x), is known: p(x|θ).

• Any information we have about θ prior to collecting samples is contained in a known prior density p(θ).

• Observation of samples converts this to a posterior, p(θ|D), which we hope is peaked around the true value of θ.

• Our goal is to estimate a parameter vector:

dDpDp )()( ,xx

• We can write the joint distribution as a product:

dDpp

dDpDpDp

)(

),()(

()

()

xxx

because the samples are drawn independently.

The Parameter Distribution

)( Dp x)Dp (

• This equation links the class-conditional density

to the posterior, . But numerical solutions are typically required!

ECE 8527: Lecture 05, Slide 5

• Case: only mean unknown ),()( 2 Np x

• Known prior density: ),()( 200 Np

• Using Bayes formula:

)()(

])()([

)()()()(

)()()(

)(

)()()()(

1

pp

pDp

dpDppDp

DppDp

Dp

pDpDpDp

n

kk

x

• Rationale: Once a value of μ is

known, the density for x is

completely known. α is a

normalization factor that

depends on the data, D.

Univariate Gaussian Case

ECE 8527: Lecture 05, Slide 6

• Applying our Gaussian assumptions:

n

k

k

n

k

kn

k

k

n

k

kk

n

k

k

n

k

kDp

12

2

220

020

2

12

2

220

020

2

12

2

20

20

12

2

22

2

20

200

2

1

22

0

0

2

0

0

01

2

)2(2

21exp

)2(2

21exp

21exp

22

21exp

21exp

21exp

21

21exp

21|

x

xx

xx

x

x

Univariate Gaussian Case

ECE 8527: Lecture 05, Slide 7

• Now we need to work this into a simpler form:

Univariate Gaussian Case (Cont.)

n

kkn

n

n

kk

n

kk

n

k

kn

k

n

k

k

nwhere

nn

nn

n

Dp

1

20

02

220

2

20

0

12

220

2

20

0

122

0

2

2

2

20

0

122

0

2

12

2

12

2

220

020

2

1ˆ

)ˆ(12121(exp

12121(exp

2221exp

2221exp

)2(2

21exp|

x

x

x

x

x

ECE 8527: Lecture 05, Slide 8

Univariate Gaussian Case (Cont.)• p(μ|D) is an exponential of a quadratic function, which makes it a normal

distribution. Because this is true for any n, it is referred to as a reproducing density.

• p(μ) is referred to as a conjugate prior.

• Write p(μ|D) ~ N(μn,σn2):

• Expand the quadratic term:

• Equate coefficients of our two functions:

])(21exp[

21)( 2

n

n

nDp

)]2

(21exp[

21])(

21exp[

21)( 2

222

n

nn

nn

n

n

Dp

20

02

220

2

2

22

)ˆ(12121exp

221exp

21

n

n

nn

n

nn

ECE 8527: Lecture 05, Slide 9

Univariate Gaussian Case (Cont.)

• Rearrange terms so that the dependencies on μ are clear:

• Associate terms related to σ2 and μ:

• There is actually a third equation involving terms not related to :

but we can ignore this since it is not a function of μ and is a complicated equation to solve.

20

02

220

2

22

22

2

)ˆ(12121exp

2121exp

21exp

21

n

n

n

nn

n

n

nn

n

n

n

nn

n

n

or

21

21

21exp

21

21exp

21

02

2

2

2

ECE 8527: Lecture 05, Slide 10

• Two equations and two unknowns. Solve for μn and σn2. First, solve for μn

2 :

220

220

220

20

2

20

2

2

11

nnnn

Univariate Gaussian Case (Cont.)

• Next, solve for μn:

• Summarizing:

220

2202

0220

2

220

20 ˆ)(

n

nnn

n

nn

220

2

0220

20

220

220

20

0220

220

2

20

2

02

2

ˆ

1)(ˆ

)(ˆ

nnn

nnn

n

n

n

nnnn

ECE 8527: Lecture 05, Slide 11

• μn represents our best guess after n samples.

• σn2 represents our uncertainty about this guess.

• σn2 approaches σ2/n for large n – each additional observation decreases our

uncertainty.

• The posterior, p(μ|D), becomes more sharply peaked as n grows large. This is known as Bayesian learning.

Bayesian Learning

ECE 8527: Lecture 05, Slide 12

• How do we obtain p(x|D) (derivation is tedious):

),(])(21exp[

21

])(21exp[

21][)(

21exp[

21

)()(

222

22

nn

n

n

n

n

n

fx

dx

dDpxpDxp

()

where:

df

n

nn

n

nn

2

22

22

22

22)(

21exp[),( x

• Note that: ),(~)|( 22nnNDp x

• The conditional mean, μn, is treated as the true mean.• p(x|D) and P(ωj) can be used to design the classifier.

Class-Conditional Density

ECE 8527: Lecture 05, Slide 13

• Applying Bayes formula:

))(2)((21exp

)|(|

010

1

110

1

1

n

ik

n

kk

n

ppDp

x

x

tt

which has the form:

)()(21exp| 1

nnDp t

• Once again: ),(~| nnNDp

and we have a reproducing density.

Multivariate Case

• Assume:

where are assumed to be known.

),(~)(and),(~)( 00 NpNp x

00 , and

ECE 8527: Lecture 05, Slide 14

• Equating coefficients between the two Gaussians:

n

kkn

nnn

n

n

n

n

1

010

11

10

11

1ˆ

ˆ

x

• The solution to these equations is:

nn

nnn

n

nn

1)1(

)1(1ˆ)1(

100

01

01

00

• It also follows that: ),(~)|( nnNDp x

Estimation Equations

ECE 8527: Lecture 05, Slide 15

• p(x | D) computation can be applied to any situation in which the unknown density can be parameterized.

• The basic assumptions are:

The form of p(x | θ) is assumed known, but the value of θ is not known exactly.

Our knowledge about θ is assumed to be contained in a known prior density p(θ).

The rest of our knowledge about θ is contained in a set D of n random variables x1, x2, …, xn drawn independently according to the unknown probability density function p(x).

General Theory

ECE 8527: Lecture 05, Slide 16

Formal Solution• The posterior is given by:

• Using Bayes formula, we can write p(D| θ ) as:

• and by the independence assumption:

• This constitutes the formal solution to the problem because we have an expression for the probability of the data given the parameters.

• This also illuminates the relation to the maximum likelihood estimate:

• Suppose p(D| θ ) reaches a sharp peak at .

dDppDp )|()|()|( xx

dppppDp

)|()|()|(

DD

n

kpDp1()|( kx

θθ ˆ

ECE 8527: Lecture 05, Slide 17

Comparison to Maximum Likelihood

• This also illuminates the relation to the maximum likelihood estimate:

Suppose p(D| θ ) reaches a sharp peak at .

p(θ| D) will also peak at the same place if p(θ) is well-behaved.

p(x|D) will be approximately , which is the ML result.

If the peak of p(D| θ ) is very sharp, then the influence of prior information on the uncertainty of the true value of θ can be ignored.

However, the Bayes solution tells us how to use all of the available information to compute the desired density p(x|D).

θθ ˆ

|xp

ECE 8527: Lecture 05, Slide 18

Recursive Bayes Incremental Learning

• To indicate explicitly the dependence on the number of samples, let:

• We can then write our expression for p(D| θ ):

where .

• We can write the posterior density using a recursive relation:

• where .

• This is called the Recursive Bayes Incremental Learning because we have a method for incrementally updating our estimates.

dDpxpDpxp

dpDppDpDp

nn

nn

n

nn

)|()|()|()|(

)|()|()|(

1

1

)()|( 0 pDp

nnD xxx ,...,, 21

)|()|()|( 1 nn

n DppDp x

)()|( 0 pDp

ECE 8527: Lecture 05, Slide 19

When do ML and Bayesian Estimation Differ?

• For infinite amounts of data, the solutions converge. However, limited data is always a problem.

• If prior information is reliable, a Bayesian estimate can be superior.

• Bayesian estimates for uniform priors are similar to an ML solution.

• If p(θ| D) is broad or asymmetric around the true value, the approaches are likely to produce different solutions.

• When designing a classifier using these techniques, there are three sources of error:

Bayes Error: the error due to overlapping distributions

Model Error: the error due to an incorrect model or incorrect assumption about the parametric form.

Estimation Error: the error arising from the fact that the parameters are estimated from a finite amount of data.

ECE 8527: Lecture 05, Slide 20

Noninformative Priors and Invariance

• The information about the prior is based on the designer’s knowledge of the problem domain.

• We expect the prior distributions to be “translation and scale invariance” – they should not depend on the actual value of the parameter.

• A prior that satisfies this property is referred to as a “noninformative prior”:

The Bayesian approach remains applicable even when little or no prior information is available.

Such situations can be handled by choosing a prior density giving equal weight to all possible values of θ.

Priors that seemingly impart no prior preference, the so-called noninformative priors, also arise when the prior is required to be invariant under certain transformations.

Frequently, the desire to treat all possible values of θ equitably leads to priors with infinite mass. Such noninformative priors are called improper priors.

ECE 8527: Lecture 05, Slide 21

Example of Noninformative Priors

• For example, if we assume the prior distribution of a mean of a continuous

random variable is independent of the choice of the origin, the only prior

that could satisfy this is a uniform distribution (which isn’t possible).

• Consider a parameter σ, and a transformation of this variable:

new variable, . Suppose we also scale by a positive constant:

. A noninformative prior on σ is the inverse distribution

p(σ) = 1/ σ, which is also improper.

lnln)ln(ˆ aa

)ln(~

ECE 8527: Lecture 05, Slide 22

• Direct computation of p(D|θ) and p(θ|D) for large data sets is challenging(e.g. neural networks)

• We need a parametric form for p(x|θ) (e.g., Gaussian)

• Gaussian case: computation of the sample mean and covariance, which was straightforward, contained all the information relevant to estimating the unknown population mean and covariance.

• This property exists for other distributions.

• A sufficient statistic is a function s of the samples D that contains all the information relevant to a parameter, θ.

• A statistic, s, is said to be sufficient for θ if p(D|s,θ) is independent of θ:

)|()|(

)|(),|(),|( spsDp

spsDpDsp

Sufficient Statistics

ECE 8527: Lecture 05, Slide 23

• Theorem: A statistic, s, is sufficient for θ, if and only if p(D|θ) can be written as: .

• There are many ways to formulate sufficient statistics(e.g., define a vector of the samples themselves).

• Useful only when the function g() and the sufficient statistic are simple(e.g., sample mean calculation).

• The factoring of p(D|θ) is not unique:

)(),()|( DhsgDp

)sfDhDhsgsfsg (/)()(),()(),(

• Define a kernel density invariant to scaling:

• Significance: most practical applications of parameter estimation involve simple sufficient statistics and simple kernel densities.

dsg

sgsg),(),(),(~

The Factorization Theorem

ECE 8527: Lecture 05, Slide 24

• This isolates the θ dependence in the first term, and hence, the sample mean

is a sufficient statistic using the Factorization Theorem.

• The kernel is:

nt

ndn ng

~1~

21exp

21,~~ 1

2/12/

Gaussian Distributions

n

kk

tkd

n

kk

tt

n

k

tkk

ttd

kt

kn

k d

n

Dp

1

12/12/

1

11

1

1112/12/

1

1 2/12/

21exp

21

2exp

21exp

21

21exp

21)(

xx

x

xx

xx

ECE 8527: Lecture 05, Slide 25

• This can be generalized: )]()()(exp[)|( cbaxx tp

and: )(),(])()()(exp[)|(1

DhgnDpn

kkk

t s

xxcban

1k

• Examples:

The Exponential Family

ECE 8527: Lecture 05, Slide 26

Summary• Introduction of Bayesian parameter estimation.• The role of the class-conditional distribution in a Bayesian estimate.• Estimation of the posterior and probability density function assuming the

only unknown parameter is the mean, and the conditional density of the “features” given the mean, p(x|θ), can be modeled as a Gaussian distribution.

• Bayesian estimates of the mean for the multivariate Gaussian case.• General theory for Bayesian estimation.• Comparison to maximum likelihood estimates.• Recursive Bayesian incremental learning.• Noninformative priors.• Sufficient statistics• Kernel density.

ECE 8527: Lecture 05, Slide 27

• Getting ahead a bit, let’s see how we can put these ideas to work on a simple example due to David MacKay, and explained by Jon Hamaker.

“The Euro Coin”