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『パターン認識と機械学習』の輪講で用いた資料。
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Pattern Recognition and Machine Learning §3.3 (Bayesian Linear Regression)
Christopher M. Bishop
Introduced by: Yusuke Oda (NAIST) @odashi_t
2013/6/5 2013 © Yusuke Oda AHC-Lab, IS, NAIST 1
Agenda
3.3 Bayesian Linear Regression ベイズ線形回帰
– 3.3.1 Parameter distribution パラメータの分布
– 3.3.2 Predictive distribution 予測分布
– 3.3.3 Equivalent kernel 等価カーネル
2013/6/5 2013 © Yusuke Oda AHC-Lab, IS, NAIST 2
Agenda
3.3 Bayesian Linear Regression ベイズ線形回帰
– 3.3.1 Parameter distribution パラメータの分布
– 3.3.2 Predictive distribution 予測分布
– 3.3.3 Equivalent kernel 等価カーネル
2013/6/5 2013 © Yusuke Oda AHC-Lab, IS, NAIST 3
Bayesian Linear Regression
Maximum Likelihood (ML) – The number of basis functions (≃ model complexity)
depends on the size of the data set.
– Adds the regularization term to control model complexity.
– How should we determine the coefficient of regularization term?
2013/6/5 2013 © Yusuke Oda AHC-Lab, IS, NAIST 4
Bayesian Linear Regression
Maximum Likelihood (ML) – Using ML to determine the coefficient of regularization term
... Bad selection
• This always leads to excessively complex models (= over-fitting)
– Using independent hold-out data to determine model complexity (See §1.3) ... Computationally expensive ... Wasteful of valuable data
2013/6/5 2013 © Yusuke Oda AHC-Lab, IS, NAIST 5
In the case of previous slide,
λ always becomes 0
when using ML to determine λ.
Bayesian Linear Regression
Bayesian treatment of linear regression – Avoids the over-fitting problem of ML.
– Leads to automatic methods of determining model complexity using the training data alone.
What we do? – Introduces the prior distribution and likelihood .
• Assumes the model parameter as proberbility function.
– Calculates the posterior distribution using the Bayes' theorem:
2013/6/5 2013 © Yusuke Oda AHC-Lab, IS, NAIST 6
Agenda
3.3 Bayesian Linear Regression ベイズ線形回帰
– 3.3.1 Parameter distribution パラメータの分布
– 3.3.2 Predictive distribution 予測分布
– 3.3.3 Equivalent kernel 等価カーネル
2013/6/5 2013 © Yusuke Oda AHC-Lab, IS, NAIST 7
Note: Marginal / Conditional Gaussians
Marginal Gaussian distribution for
Conditional Gaussian distribution for given
Marginal distribution of
Conditional distribution of given
2013/6/5 2013 © Yusuke Oda AHC-Lab, IS, NAIST 8
Given:
Then:
where
Parameter Distribution
Remember the likelihood function given by §3.1.1:
– This is the exponential of quadratic function of
The corresponding conjugate prior is given by a Gaussian distribution:
2013/6/5 2013 © Yusuke Oda AHC-Lab, IS, NAIST 9
known parameter
Parameter Distribution
Now given:
Then the posterior distribution is shown by using (2.116):
where
2013/6/5 2013 © Yusuke Oda AHC-Lab, IS, NAIST 10
Online Learning - Parameter Distribution
If data points arrive sequentially, the design matrix has only 1 row:
Assuming that are the n-th input data then we can obtain the formula for online learning:
where
In addition,
2013/6/5 2013 © Yusuke Oda AHC-Lab, IS, NAIST 11
Easy Gaussian Prior - Parameter Distribution
If the prior distribution is a zero-mean isotropic Gaussian governed by a single precision parameter :
The corresponding posterior distribution is also given:
where
2013/6/5 2013 © Yusuke Oda AHC-Lab, IS, NAIST 12
Relationship with MSSE - Parameter Distribution
The log of the posterior distribution is given:
If prior distribution is given by (3.52), this result is shown:
– Maximization of (3.55) with respect to
– Minimization of the sum-of-squares error (MSSE) function with the addition of a quadratic regularization term
2013/6/5 2013 © Yusuke Oda AHC-Lab, IS, NAIST 13
Equivalent
Example - Parameter Distribution
Straight-line fitting – Model function:
– True function:
– Error:
– Goal: To recover the values of
from such data
– Prior distribution:
2013/6/5 2013 © Yusuke Oda AHC-Lab, IS, NAIST 14
Generalized Gaussian Prior - Parameter Distribution
We can generalize the Gaussian prior about exponent.
In which corresponds to the Gaussian and only in the case is the prior conjugate to the (3.10).
2013/6/5 2013 © Yusuke Oda AHC-Lab, IS, NAIST 15
Agenda
3.3 Bayesian Linear Regression ベイズ線形回帰
– 3.3.1 Parameter distribution パラメータの分布
– 3.3.2 Predictive distribution 予測分布
– 3.3.3 Equivalent kernel 等価カーネル
2013/6/5 2013 © Yusuke Oda AHC-Lab, IS, NAIST 16
Predictive Distribution
2013/6/5 2013 © Yusuke Oda AHC-Lab, IS, NAIST 17
Let's consider that making predictions of directly for new values of .
In order to obtain it, we need to evaluate the predictive distribution:
This formula is tipically written:
Marginalization arround
(summing out )
Predictive Distribution
The conditional distribution of the target variable is given:
And the posterior weight distribution is given:
Accordingly, the result of (3.57) is shown by using (2.115):
where
2013/6/5 2013 © Yusuke Oda AHC-Lab, IS, NAIST 18
Predictive Distribution
Now we discuss the variance of predictive distribution:
– As additional data points are observed, the posterior distribution becomes narrower:
– 2nd term of the(3.59) goes zero in the limit :
2013/6/5 2013 © Yusuke Oda AHC-Lab, IS, NAIST 19
Addictive noise
goverened by the parameter . This term depends on the mapping vector
. of each data point .
Predictive Distribution
2013/6/5 2013 © Yusuke Oda AHC-Lab, IS, NAIST 20
Example - Predictive Distribution
Gaussian regression with sine curve – Basis functions: 9 Gaussian curves
2013/6/5 2013 © Yusuke Oda AHC-Lab, IS, NAIST 21
Mean of predictive distribution
Standard deviation of
predictive distribution
Example - Predictive Distribution
Gaussian regression with sine curve
2013/6/5 2013 © Yusuke Oda AHC-Lab, IS, NAIST 22
Example - Predictive Distribution
Gaussian regression with sine curve
2013/6/5 2013 © Yusuke Oda AHC-Lab, IS, NAIST 23
Problem of Localized Basis - Predictive Distribution
Polynominal regression
Gaussian regression
2013/6/5 2013 © Yusuke Oda AHC-Lab, IS, NAIST 24
Which is better?
Problem of Localized Basis - Predictive Distribution
If we used localized basis function such as Gaussians, then in regions away from the basis function centers the contribution from the 2nd term in the (3.59) will goes zero.
Accordingly, the predictive variance becomes only the noise contribution . But it is not good result.
2013/6/5 2013 © Yusuke Oda AHC-Lab, IS, NAIST 25
Large contribution
Small contribution
Problem of Localized Basis - Predictive Distribution
This problem (arising from choosing localized basis function) can be avoided by adopting an alternative Bayesian approach to regression known as a Gaussian process.
– See §6.4.
2013/6/5 2013 © Yusuke Oda AHC-Lab, IS, NAIST 26
Case of Unknown Precision - Predictive Distribution
If both and are treated as unknown then we can introduce a conjugate prior distribution and corresponding posterior distribution as Gaussian-gamma distribution:
And then the predictive distribution is given:
2013/6/5 2013 © Yusuke Oda AHC-Lab, IS, NAIST 27
Agenda
3.3 Bayesian Linear Regression ベイズ線形回帰
– 3.3.1 Parameter distribution パラメータの分布
– 3.3.2 Predictive distribution 予測分布
– 3.3.3 Equivalent kernel 等価カーネル
2013/6/5 2013 © Yusuke Oda AHC-Lab, IS, NAIST 28
Equivalent Kernel
2013/6/5 2013 © Yusuke Oda AHC-Lab, IS, NAIST 29
If we substitute the posterior mean solution (3.53) into the expression (3.3), the predictive mean can be written:
This formula can assume the linear combination of :
Equivalent Kernel
Where the coefficients of each are given:
This function is calld smoother matrix or equivalent kernel.
Regression functions which make predictions by taking linear combinations of the training set target values are known as linear smoothers.
We also predict for new input vector using equivalent kernel, instead of calculating parameters of basis functions.
2013/6/5 2013 © Yusuke Oda AHC-Lab, IS, NAIST 30
Example 1 - Equivalent Kernel
Equivalent kernel with Gaussian regression
Equivalen kernel depends on the set of basis function and the data set.
2013/6/5 2013 © Yusuke Oda AHC-Lab, IS, NAIST 31
Equivalent Kernel
Equivalent kernel means the contribution of each data point for predictive mean.
The covariance between and can be shown by equivalent kernel:
2013/6/5 2013 © Yusuke Oda AHC-Lab, IS, NAIST 32
Large contribution
Small contribution
Properties of Equivalent Kernel - Equivalent Kernel
Equivalent kernel have localization property even if any basis functions are not localized.
Sum of equivalent kernel equals 1 for all :
2013/6/5 2013 © Yusuke Oda AHC-Lab, IS, NAIST 33
Polynominal Sigmoid
Example 2 - Equivalent Kernel
Equivalent kernel with polynominal regression
– Moving parameter:
2013/6/5 2013 © Yusuke Oda AHC-Lab, IS, NAIST 34
Example 2 - Equivalent Kernel
Equivalent kernel with polynominal regression
– Moving parameter:
2013/6/5 2013 © Yusuke Oda AHC-Lab, IS, NAIST 35
Example 2 - Equivalent Kernel
Equivalent kernel with polynominal regression
– Moving parameter:
2013/6/5 2013 © Yusuke Oda AHC-Lab, IS, NAIST 36
Properties of Equivalent Kernel - Equivalent Kernel
Equivalent kernel satisfies an important property shared by kernel functions in general: – Kernel function can be expressed in the form of an inner product with
respect to a vector of nonlinear functions:
– In the case of equivalent kernel, is given below:
2013/6/5 2013 © Yusuke Oda AHC-Lab, IS, NAIST 37
Thank you!
2013/6/5 2013 © Yusuke Oda AHC-Lab, IS, NAIST 38
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