The Likelihood Function
Introduction to Likelihood Statistics Edward L. Robinson* Department of Astronomy and McDonald Observatory University of Texas at Austin 1. The likelihood function. 2. Use of the likelihood function to model data. 3. Comparison to standard frequentist and Bayesean statistics. *Look for: “Data Analysis for Scientists and Engineers” Princeton University Press, Sept 2016. The Likelihood Function Let a probability distribution function for ⇠ have m + 1 • parameters aj f(⇠,a , a , , a )=f(⇠,~a), 0 1 ··· m The joint probability distribution for n samples of ⇠ is f(⇠ ,⇠ , ,⇠ , a , a , , a )=f(⇠~,~a). 1 2 ··· n 0 1 ··· m Now make measurements. For each variable ⇠ there is a measured • i value xi. To obtain the likelihood function L(~x,~a), replace each variable ⇠ with • i the numerical value of the corresponding data point xi: L(~x,~a) f(~x,~a)=f(x , x , , x ,~a). ⌘ 1 2 ··· n In the likelihood function the ~x are known and fixed, while the ~aare the variables. A Simple Example Suppose the probability • distribution for the data is 2 a⇠ f(⇠,a)=a ⇠e− . Measure a single data point. It • turns out to be x = 2. The likelihood function is • 2 2a L(x = 2, a)=2a e− . A Somewhat More Realistic Example Suppose we have n independent data points, each drawn from the same probability distribution 2 a⇠ f(⇠,a)=a ⇠e− . The joint probability distribution is f(⇠~,a)=f(⇠ , a)f(⇠ , a) f(⇠ , a) 1 2 ··· n n 2 a⇠i = a ⇠ie− . Yi=1 The data points are xi. The resulting likelihood function is n 2 axi L(~x, a)= a xie− .
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