Density Estimation: ML, MAP, Bayesian estimation
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1 Density Estimation: ML, MAP, Bayesian estimation CE-725: Statistical Pattern Recognition Sharif University of Technology Spring 2013 Soleymani
2 Outline Introduction Maximum-Likelihood Estimation Maximum A Posteriori Estimation Bayesian Estimation 2
3 Density Estimation Estimating the probability density function set of data points drawn from it.,givena Main approaches of density estimation: Parametric: assuming a parameterized model for density function A number of parameters are optimized by fitting the model to the data set Nonparametric (Instance-based): No specific parametric model is assumed for density function The form of the density function is determined entirely by the data 3
4 Class-Conditional Densities We usually do not know the class-conditional densities ( ). However, we might have prior knowledge about: Functional forms of these densities Ranges for the values of their unknown parameters We can separate training data of different classes and use the set containing training samples of class to estimate ( ) =(, ) Estimating ( ) from can be considered as an unsupervised density estimation problem 4
5 Parametric Density Estimation Assume that in terms of a specific functional form which has a number of adjustable parameters. Example: a multivariate Gaussian distribution Methods for parameter estimation Maximum likelihood estimation Maximum A Posteriori (MAP) estimation Bayesian estimation 5
6 Maximum Likelihood Estimation (MLE) Likelihood is the conditional probability of observations () () () given the value of parameters Assuming i.i.d. observations (statistically independent, identically distributed samples) () likelihood of w.r.t. the samples Maximum Likelihood estimation 6
7 Maximum Likelihood Estimation (MLE) best agrees with the observed samples 7
8 Maximum Likelihood Estimation (MLE) () () () Thus, we solve to find global optimum 8
9 MLE Gaussian: Unknown / () 9
10 MLE Gaussian Case: Unknown and =, 10
11 Maximum A Posteriori (MAP) Estimation MAP estimation Since Example of prior distribution: 11
12 Maximum A Posteriori (MAP) Estimation Given a set of observations and a prior distribution on parameters, the parameter vector that maximizes is found. 12
13 MAP Estimation Gaussian: Unknown ( )~(, ) ( )~(, ) is the only unknown parameter and are known 13 ln 1 = () = =0 1+ 1or = =
14 Bayesian Estimation Given:samples pdf, the form of the density, a priori information about Goal: compute the conditional pdf of. as an estimate =, = Analytical solutions exist only for very special forms of the involved functions 14 If we know the value of the parameters, we know exactly the distribution of
15 Bayesian Estimation Parameters are considered as a vector of random variables with a priori distribution Bayesian estimation utilizes the available prior information about the unknown parameter As opposed to ML and MAP estimation, it does not seek a specific point estimate of the unknown parameter vector The observed samples convert the prior densities a posterior density To find the conditional pdf ( ), we must first specify Then, is found as = into 15
16 Bayesian Estimation Gaussian: Unknown (known ) ~(, ) ()~(, ) = () ( () = 1 2 exp 1 2 = exp 1 2 = exp exp
17 Bayesian Estimation Gaussian: Unknown (known ) ~(, ) (): conjugate prior = = + = = 1 2 exp 1 2 +, ~(, + ) 17
18 Bayesian Estimation Gaussian: Unknown (known ) ( ) More samples sharper ( ) 18
19 Some Related Definitions Conjugate Priors We consider a form of prior distribution that has a simple interpretation as well as some useful analytical properties Choosing a prior such that the posterior distribution that is proportional to ( )() will have the same functional form as the prior. Bayesian learning When densities converges to a Dirac delta function centered about the true parameter value 19
20 ML, MAP, and Bayesian Estimation If has a sharp peak at (i.e., ), then In this case, the Bayesian estimation will be approximately equal to the MAP estimation. If is concentrated around a sharp peak and () is broad enough around this peak, the ML, MAP, and Bayesian estimations yield approximately the same result. All three methods asymptotically ( same estimate )resultsinthe 20
21 Bayesian Estimation: Example =,, () =4 =7 21
22 Bayesian Estimation: Example = 22
23 Summary ML and MAP result in a single (point) estimate of the unknown parameters vector. More simple and interpretable than Bayesian estimation Bayesian approach estimates a distribution using all the available information: expected to give better results needs higher computational complexity Bayesian methods have gained a lot of popularity over the recent decade due to the advances in computer technology. All three methods asymptotically ( ) results in the same estimate. 23
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