Bayesian statistics DS GA 1002 Statistical and Mathematical Models - - PowerPoint PPT Presentation

Bayesian statistics

DS GA 1002 Statistical and Mathematical Models

http://www.cims.nyu.edu/~cfgranda/pages/DSGA1002_fall15 Carlos Fernandez-Granda

Frequentist vs Bayesian statistics

In frequentist statistics the data are modeled as realizations from a distribution that depends on deterministic parameters In Bayesian statistics the parameters are modeled as random variables This allows to quantify our prior uncertainty and incorporate additional information

Learning Bayesian models Conjugate priors Bayesian estimators

Prior distribution and likelihood

The data x ∈ Rn are a realization of a random vector X, which depends on a vector of parameters Θ Modeling choices:

◮ Prior distribution: Distribution of

Θ encoding our uncertainty about the model before seeing the data

◮ Likelihood: Conditional distribution of

X given Θ

Posterior distribution

The posterior distribution is the conditional distribution of Θ given X Evaluating the posterior at the data x allows to update our uncertainty about Θ using the data

Bernoulli distribution

Goal: Estimating Bernoulli parameter from iid data We consider two different Bayesian estimators Θ1 and Θ2:

• 1. Θ1 is a conservative estimator with a uniform prior pdf

fΘ1 (θ) =

• 1

for 0 ≤ θ ≤ 1

• therwise
• 2. Θ2 has a prior pdf skewed towards 1

fΘ2 (θ) =

• 2 θ

for 0 ≤ θ ≤ 1

• therwise

Prior distributions

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.5 1.0 1.5 2.0

Bernoulli distribution: likelihood

The data are assumed to be iid, so the likelihood is p

X|Θ (

x|θ)

Bernoulli distribution: likelihood

The data are assumed to be iid, so the likelihood is p

X|Θ (

x|θ) = θn1 (1 − θ)n0 n0 is the number of zeros and n1 the number of ones

Bernoulli distribution: posterior distribution

fΘ1|

X (θ|

x)

Bernoulli distribution: posterior distribution

fΘ1|

X (θ|

x) = fΘ1 (θ) p

X|Θ1 (

x|θ) p

X (

x)

Bernoulli distribution: posterior distribution

fΘ1|

X (θ|

x) = fΘ1 (θ) p

X|Θ1 (

x|θ) p

X (

x) = fΘ1 (θ) p

X|Θ1 (

x|θ)

• u fΘ1 (u) p

X|Θ1 (

x|u) du

Bernoulli distribution: posterior distribution

fΘ1|

X (θ|

x) = fΘ1 (θ) p

X|Θ1 (

x|θ) p

X (

x) = fΘ1 (θ) p

X|Θ1 (

x|θ)

• u fΘ1 (u) p

X|Θ1 (

x|u) du = θn1 (1 − θ)n0

• u un1 (1 − u)n0 du

Bernoulli distribution: posterior distribution

fΘ1|

X (θ|

x) = fΘ1 (θ) p

X|Θ1 (

x|θ) p

X (

x) = fΘ1 (θ) p

X|Θ1 (

x|θ)

• u fΘ1 (u) p

X|Θ1 (

x|u) du = θn1 (1 − θ)n0

• u un1 (1 − u)n0 du

= θn1 (1 − θ)n0 β (n1 + 1, n0 + 1) β (a, b) :=

• u

ua−1 (1 − u)b−1 du

Bernoulli distribution: posterior distribution

fΘ2|

X (θ|

x)

Bernoulli distribution: posterior distribution

fΘ2|

X (θ|

x) = fΘ2 (θ) p

X|Θ2 (

x|θ) p

X (

x)

Bernoulli distribution: posterior distribution

fΘ2|

X (θ|

x) = fΘ2 (θ) p

X|Θ2 (

x|θ) p

X (

x) = θn1+1 (1 − θ)n0

• u un1+1 (1 − u)n0 du

Bernoulli distribution: posterior distribution

fΘ2|

X (θ|

x) = fΘ2 (θ) p

X|Θ2 (

x|θ) p

X (

x) = θn1+1 (1 − θ)n0

• u un1+1 (1 − u)n0 du

= θn1+1 (1 − θ)n0 β (n1 + 2, n0 + 1) β (a, b) :=

• u

ua−1 (1 − u)b−1 du

Bernoulli distribution: n0 = 1, n1 = 3

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.5 1.0 1.5 2.0 2.5

Bernoulli distribution: n0 = 3, n1 = 1

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.5 1.0 1.5 2.0

Bernoulli distribution: n0 = 91, n1 = 9

0.0 0.2 0.4 0.6 0.8 1.0 2 4 6 8 10 12 14

Posterior mean (uniform prior) Posterior mean (skewed prior) ML estimator

Learning Bayesian models Conjugate priors Bayesian estimators

Beta distribution

The pdf of a beta distribution with parameters a and b is defined as fβ (θ; a, b) := θa−1(1−θ)b−1

β(a,b)

, if 0 ≤ θ ≤ 1,

• therwise

β (a, b) :=

• u

ua−1 (1 − u)b−1 du

Learning a Bernoulli distribution

The first prior is beta with parameters a = 1 and b = 1 The second prior is beta with parameters a = 2 and b = 1 The posteriors are beta with parameters a = n1 + 1, b = n0 + 1 and a = n1 + 2, b = n0 + 1 respectively

Conjugate priors

A conjugate family of distributions for a certain likelihood satisfies the following property: If the prior belongs to the family, the posterior also belongs to the family Beta distributions are conjugate priors when the likelihood is binomial

The beta distribution is conjugate to the binomial likelihood

Θ is beta with parameters a and b X is binomial with parameters n and Θ fΘ | X (θ | x)

The beta distribution is conjugate to the binomial likelihood

Θ is beta with parameters a and b X is binomial with parameters n and Θ fΘ | X (θ | x) = fΘ (θ) pX | Θ (x | θ) pX (x)

The beta distribution is conjugate to the binomial likelihood

Θ is beta with parameters a and b X is binomial with parameters n and Θ fΘ | X (θ | x) = fΘ (θ) pX | Θ (x | θ) pX (x) = fΘ (θ) pX | Θ (x | θ)

• u fΘ (u) pX | Θ (x | u) du

The beta distribution is conjugate to the binomial likelihood

Θ is beta with parameters a and b X is binomial with parameters n and Θ fΘ | X (θ | x) = fΘ (θ) pX | Θ (x | θ) pX (x) = fΘ (θ) pX | Θ (x | θ)

• u fΘ (u) pX | Θ (x | u) du

= θa−1 (1 − θ)b−1 n

x

• θx (1 − θ)n−x
• u ua−1 (1 − u)b−1 n

x

• ux (1 − u)n−x du

The beta distribution is conjugate to the binomial likelihood

Θ is beta with parameters a and b X is binomial with parameters n and Θ fΘ | X (θ | x) = fΘ (θ) pX | Θ (x | θ) pX (x) = fΘ (θ) pX | Θ (x | θ)

• u fΘ (u) pX | Θ (x | u) du

= θa−1 (1 − θ)b−1 n

x

• θx (1 − θ)n−x
• u ua−1 (1 − u)b−1 n

x

• ux (1 − u)n−x du

= θx+a−1 (1 − θ)n−x+b−1

• u ux+a−1 (1 − u)n−x+b−1 du

The beta distribution is conjugate to the binomial likelihood

Θ is beta with parameters a and b X is binomial with parameters n and Θ fΘ | X (θ | x) = fΘ (θ) pX | Θ (x | θ) pX (x) = fΘ (θ) pX | Θ (x | θ)

• u fΘ (u) pX | Θ (x | u) du

= θa−1 (1 − θ)b−1 n

x

• θx (1 − θ)n−x
• u ua−1 (1 − u)b−1 n

x

• ux (1 − u)n−x du

= θx+a−1 (1 − θ)n−x+b−1

• u ux+a−1 (1 − u)n−x+b−1 du

= fβ (θ; x + a, n − x + b)

Poll in New Mexico

449 participants, 227 people intend to vote for Clinton and 202 for Trump Probability that Trump wins in New Mexico? Assumptions:

◮ Fraction of Trump voters is modeled as a random variable Θ ◮ Poll participants are selected uniformly at random with replacement ◮ Number of Trump voters in the poll is binomial with parameters

n = 449 and p = Θ

Poll in New Mexico

◮ Prior is uniform, so beta with parameters a = 1 and b = 1 ◮ Likelihood is binomial ◮ Posterior is beta with parameters a = 202 + 1 and b = 227 + 1 ◮ The probability that Trump wins in New Mexico is the probability that

Θ given the data is greater than 0.5

Poll in New Mexico

0.35 0.40 0.45 0.50 0.55 0.60 2 4 6 8 10 12 14 16 18

88.6% 11.4%

Learning Bayesian models Conjugate priors Bayesian estimators

Bayesian estimators

What estimator should be use? Two main options:

◮ The posterior mean ◮ The posterior mode

Posterior mean

Mean of the posterior distribution θMMSE ( x) := E

• Θ|

X = x

• Minimum mean-square-error (MMSE) estimate

For any arbitrary estimator θother ( x), E

• θother(

X) − Θ 2 ≥ E

• θMMSE(

X) − Θ 2

Posterior mean

E

• θother(

X) − Θ 2

• X =

x

Posterior mean

E

• θother(

X) − Θ 2

• X =

x

• = E
• θother(

X) − θMMSE( X) + θMMSE( X) − Θ 2

• X =

x

Posterior mean

E

• θother(

X) − Θ 2

• X =

x

• = E
• θother(

X) − θMMSE( X) + θMMSE( X) − Θ 2

• X =

x

• = (θother(

x) − θMMSE( x))2 + E θMMSE( X) − Θ 2

• X =

x

• + 2 (θother(

x) − θMMSE( x)) E

• θMMSE(

x) − E

• Θ|

X = x

Posterior mean

E

• θother(

X) − Θ 2

• X =

x

• = E
• θother(

X) − θMMSE( X) + θMMSE( X) − Θ 2

• X =

x

• = (θother(

x) − θMMSE( x))2 + E θMMSE( X) − Θ 2

• X =

x

• + 2 (θother(

x) − θMMSE( x)) E

• θMMSE(

x) − E

• Θ|

X = x

• = (θother(

x) − θMMSE( x))2 + E θMMSE( X) − Θ 2

• X =

x

Posterior mean

By iterated expectation, E

• θother(

X) − Θ 2 = E

• E
• θother(

X) − Θ 2

• X

Posterior mean

By iterated expectation, E

• θother(

X) − Θ 2 = E

• E
• θother(

X) − Θ 2

• X
• = E
• θother(

X) − θMMSE( X) 2 + E

• E

θMMSE( X) − Θ 2

• X

Posterior mean

By iterated expectation, E

• θother(

X) − Θ 2 = E

• E
• θother(

X) − Θ 2

• X
• = E
• θother(

X) − θMMSE( X) 2 + E

• E

θMMSE( X) − Θ 2

• X
• = E
• θother(

X) − θMMSE( X) 2 + E

• θMMSE(

X) − Θ 2

Posterior mean

By iterated expectation, E

• θother(

X) − Θ 2 = E

• E
• θother(

X) − Θ 2

• X
• = E
• θother(

X) − θMMSE( X) 2 + E

• E

θMMSE( X) − Θ 2

• X
• = E
• θother(

X) − θMMSE( X) 2 + E

• θMMSE(

X) − Θ 2 ≥ E

• θMMSE(

X) − Θ 2

Bernoulli distribution: n0 = 1, n1 = 3

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.5 1.0 1.5 2.0 2.5

Bernoulli distribution: n0 = 3, n1 = 1

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.5 1.0 1.5 2.0

Bernoulli distribution: n0 = 91, n1 = 9

0.0 0.2 0.4 0.6 0.8 1.0 2 4 6 8 10 12 14

Posterior mean (uniform prior) Posterior mean (skewed prior) ML estimator

Posterior mode

The maximum-a-posteriori (MAP) estimator is the mode of the posterior distribution θMAP ( x) := arg max

• θ

p

Θ | X

• θ |

x

• if

Θ is discrete and θMAP ( x) := arg max

• θ

f

Θ | X

• θ |

x

• if

Θ is continuous

Maximum-likelihood estimator

If the prior is uniform the ML estimator coincides with the MAP estimator arg max

• θ

f

Θ | X

• θ|

x

Maximum-likelihood estimator

If the prior is uniform the ML estimator coincides with the MAP estimator arg max

• θ

f

Θ | X

• θ|

x

• = arg max
• θ

f

Θ

• θ
• f

X| Θ

• x|

θ

• u f

Θ (u) f X| Θ (

x|u) du

Maximum-likelihood estimator

If the prior is uniform the ML estimator coincides with the MAP estimator arg max

• θ

f

Θ | X

• θ|

x

• = arg max
• θ

f

Θ

• θ
• f

X| Θ

• x|

θ

• u f

Θ (u) f X| Θ (

x|u) du = arg max

• θ

f

X| Θ

• x|

θ

Maximum-likelihood estimator

If the prior is uniform the ML estimator coincides with the MAP estimator arg max

• θ

f

Θ | X

• θ|

x

• = arg max
• θ

f

Θ

• θ
• f

X| Θ

• x|

θ

• u f

Θ (u) f X| Θ (

x|u) du = arg max

• θ

f

X| Θ

• x|

θ

• = arg max
• θ

L

x

• θ

Maximum-likelihood estimator

If the prior is uniform the ML estimator coincides with the MAP estimator arg max

• θ

f

Θ | X

• θ|

x

• = arg max
• θ

f

Θ

• θ
• f

X| Θ

• x|

θ

• u f

Θ (u) f X| Θ (

x|u) du = arg max

• θ

f

X| Θ

• x|

θ

• = arg max
• θ

L

x

• θ
• Uniform priors are only well defined over bounded domains

Probability of error

If Θ is discrete, MAP estimator minimizes the probability of error For any arbitrary estimator θother ( x) P

• θother(

X) = Θ

• ≥ P
• θMAP(

X) = Θ

Probability of error

P

• Θ = θother(

X)

Probability of error

P

• Θ = θother(

X)

• =
• x

f

X (

x) P

• Θ = θother(

x) | X = x

• d

x

Probability of error

P

• Θ = θother(

X)

• =
• x

f

X (

x) P

• Θ = θother(

x) | X = x

• d

x =

• x

f

X (

x) p

Θ | X (θother(

x) | x) d x

Probability of error

P

• Θ = θother(

X)

• =
• x

f

X (

x) P

• Θ = θother(

x) | X = x

• d

x =

• x

f

X (

x) p

Θ | X (θother(

x) | x) d x ≤

• x

f

X (

x) p

Θ | X (θMAP(

x) | x) d x

Probability of error

P

• Θ = θother(

X)

• =
• x

f

X (

x) P

• Θ = θother(

x) | X = x

• d

x =

• x

f

X (

x) p

Θ | X (θother(

x) | x) d x ≤

• x

f

X (

x) p

Θ | X (θMAP(

x) | x) d x = P

• Θ = θMAP(

X)

Sending bits

Model for communication channel: signal Θ encodes a single bit Prior knowledge indicates that a 0 is 3 times more likely than a 1 pΘ (1) = 1 4, pΘ (0) = 3 4. The channel is noisy, so we send the signal n times At the receptor we observe

• Xi = Θ +

Zi, 1 ≤ i ≤ n, where Z is iid standard Gaussian

Sending bits: ML estimator

The likelihood is equal to L

x (θ) = n

• i=1

f

Xi|Θ (

xi | θ) =

n

• i=1

1 √ 2π e− (

xi −θ)2 2

The log-likelihood is equal to log L

x (θ) = − n

• i=1

( xi − θ)2 2 − n 2 log 2π

Sending bits: ML estimator

θML ( x) = 1 if log L

x (1) = − n

• i=1
• x 2

i − 2

xi + 1 2 − n 2 log 2π ≥ −

n

• i=1
• x 2

i

2 − n 2 log 2π = log L

x (0)

Equivalently, θML ( x) =

• 1

if 1

n

n

i=1

xi > 1

2

• therwise

Sending bits: ML estimator

The probability of error is

P

• Θ = θML(

X)

Sending bits: ML estimator

The probability of error is

P

• Θ = θML(

X)

• = P
• Θ = θML(

X)

• Θ = 0
• P (Θ = 0) + P
• Θ = θML(

X)

• Θ = 1
• P (Θ = 1)

Sending bits: ML estimator

The probability of error is

P

• Θ = θML(

X)

• = P
• Θ = θML(

X)

• Θ = 0
• P (Θ = 0) + P
• Θ = θML(

X)

• Θ = 1
• P (Θ = 1)

= P

• 1

n

n

• i=1
• xi > 1

2

• Θ = 0
• P (Θ = 0) + P
• 1

n

n

• i=1
• xi < 1

2

• Θ = 1
• P (Θ = 1)

Sending bits: ML estimator

The probability of error is

P

• Θ = θML(

X)

• = P
• Θ = θML(

X)

• Θ = 0
• P (Θ = 0) + P
• Θ = θML(

X)

• Θ = 1
• P (Θ = 1)

= P

• 1

n

n

• i=1
• xi > 1

2

• Θ = 0
• P (Θ = 0) + P
• 1

n

n

• i=1
• xi < 1

2

• Θ = 1
• P (Θ = 1)

= Q √n/2

Sending bits: MAP estimator

The logarithm of the posterior is equal to log pΘ|

X (θ|

x)

Sending bits: MAP estimator

The logarithm of the posterior is equal to log pΘ|

X (θ|

x) = log n

i=1 f Xi|Θ (

xi|θ) pΘ (θ) f

X (

x)

Sending bits: MAP estimator

The logarithm of the posterior is equal to log pΘ|

X (θ|

x) = log n

i=1 f Xi|Θ (

xi|θ) pΘ (θ) f

X (

x) =

n

• i=1

log f

Xi|Θ (

xi|θ) pΘ (θ) − log f

X (

x)

Sending bits: MAP estimator

The logarithm of the posterior is equal to log pΘ|

X (θ|

x) = log n

i=1 f Xi|Θ (

xi|θ) pΘ (θ) f

X (

x) =

n

• i=1

log f

Xi|Θ (

xi|θ) pΘ (θ) − log f

X (

x) = −

n

• i=1
• x 2

i − 2

xiθ + θ2 2 − n 2 log 2π + log pΘ (θ) − log f

X (

x)

Sending bits: MAP estimator

θMAP ( x) = 1 if log pΘ|

X (1|

x) + log f

X (

x) = −

n

• i=1
• x 2

i − 2

xi + 1 2 − n 2 log 2π − log 4 ≥ −

n

• i=1
• x 2

i

2 − n 2 log 2π − log 4 + log 3 = log pΘ|

X (0|

x) + log f

X (

x) . Equivalently, θMAP ( x) =

• 1

if 1

n

n

i=1

xi > 1

2 + log 3 n ,

• therwise.

Sending bits: MAP estimator

The probability of error is

P (Θ = θMAP ( x))

Sending bits: MAP estimator

The probability of error is

P (Θ = θMAP ( x)) = P (Θ = θMAP ( x) |Θ = 0) P (Θ = 0) + P (Θ = θMAP ( x) |Θ = 1) P (Θ = 1)

Sending bits: MAP estimator

The probability of error is

P (Θ = θMAP ( x)) = P (Θ = θMAP ( x) |Θ = 0) P (Θ = 0) + P (Θ = θMAP ( x) |Θ = 1) P (Θ = 1) = P

• 1

n

n

• i=1
• xi > 1

2 + log 3 n

• Θ = 0
• P (Θ = 0)

+ P

• 1

n

n

• i=1
• xi < 1

2 + log 3 n

• Θ = 1
• P (Θ = 1)

Sending bits: MAP estimator

The probability of error is

P (Θ = θMAP ( x)) = P (Θ = θMAP ( x) |Θ = 0) P (Θ = 0) + P (Θ = θMAP ( x) |Θ = 1) P (Θ = 1) = P

• 1

n

n

• i=1
• xi > 1

2 + log 3 n

• Θ = 0
• P (Θ = 0)

+ P

• 1

n

n

• i=1
• xi < 1

2 + log 3 n

• Θ = 1
• P (Θ = 1)

= 3 4Q √n/2 + log 3 √n

• + 1

4Q √n/2 − log 3 √n

Sending bits: Probability of error

5 10 15 20

n

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35

Probability of error ML estimator MAP estimator