# series2 - Exercises Machine Learning Laboratory Dept of...

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Machine Learning Laboratory Dept. of Computer Science, ETH Z¨urich Prof. Dr. Joachim M. Buhmann Web Email questions to: Gabriel Krummenacher [email protected] Exercises Machine Learning AS 2012 Series 2, Oct 9th, 2010 (Maximum Likelihood Estimation) Master solution will be available online from Tuesday, Oct 16th. Problem 1 (Analytic MLE): In this problem, we analytically derive maximum likelihood estimators for the parameters of an example model distribution. Most textbooks, including Duda et al, discuss the Gaussian example. The distribution we consider here is called the gamma distribution . The gamma distribution is univariate (one-dimensional) and continuous. It is controlled by two parameters, the location parameter μ and the shape parameter ν . 1 For a gamma-distributed random variable X , we write X ∼ G ( μ, ν ) . G is defined by the following density function: p ( x | μ, ν ) := ν μ ν x ν - 1 Γ ( ν ) exp - νx μ , where x 0 and μ, ν > 0 . 2 Whenever ν > 1 , the gamma density has a single peak, much like a Gaussian. Unlike the Gaussian, it is not symmetric. The first two moment statistics of the gamma distribution are given by E [ X ] = μ and Var [ X ] = μ 2 ν (1) for X ∼ G ( μ, ν ) . Here are some plots which should give you a rough idea of what the gamma density may look like and how different parameter values influence its behavior: 0 0.5 1 1.5 2 2.5 3 0 0.5 1 1.5 2 2.5 0 0.5 1 1.5 2 2.5 0 0.5 1 1.5 2 2.5 3 0 0.5 1 1.5 2 2.5 3 0 0.5 1 1.5 2 2.5 Left: The plot shows the density for different values of the location parameter ( μ = 0 . 3 , 0 . 5 , 1 . 0 , 3 . 0 ), with the shape parameter fixed to ν = 2 . Since ν > 1 , the densities peak. As we increase μ , the peak moves to the right, and the curve flattens. Middle: For μ = 0 . 5 fixed, we look at different values of the shape parameter ( ν = 2 , 3 , 4 , 5 , 19 ). Again, all the densities peak, with the peak shifting to the right as we increase ν . Right: If ν < 1 , the density turns into a monotonously decreasing function. The smaller the value of ν , the sharper the curve dips towards the origin. 1. Write the general analytic procedure to obtain the maximum likelihood estimator (including logarithmic transformation) in the form of a short algorithm or recipe. A few words are enough, but be precise: Write all important mathematical operations as formulae. Assume that data is given as an i. i. d. sample

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• Fall '12
• J. M. Buhmann
• Machine Learning, Maximum likelihood, Likelihood function, maximum likelihood estimator, Shape parameter, Gaussians

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