Given a set of training samples DN x1, x2, , xN , the so called empirical distribution corresponding to DN is defined as follows where denotes Diracs delta function Show that the MLE is equivalent to minimizing the Kullback Leibler (KL) divergence between the empirical distribution and the data dis...

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Given a set of training samples DN = x1, x2, , xN , the so-called empirical distribution

Question:

Given a set of training samples DN =

x1, x2, , xN

, the so-called empirical distribution corresponding to DN is defined as follows:

image text in transcribed

where ¹º denotes Dirac’s delta function. Show that the MLE is equivalent to minimizing the Kullback–
Leibler (KL) divergence between the empirical distribution and the data distribution described by a generative model pˆ¹xº:

image text in transcribed

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Machine Learning Fundamentals A Concise Introduction

ISBN: 9781108940023

1st Edition

Authors: Hui Jiang

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Given a set of training samples DN = x1, x2, , xN , the so-called empirical distribution

Question:

S(x DN) 8(x-x1), N i=1

Step by Step Answer:

Machine Learning Fundamentals A Concise Introduction

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