lec1 - Lecture 1 Estimation theory. 1.1 Introduction Let us...

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Lecture 1 Estimation theory. 1.1 Introduction Let us consider a set X (probability space) which is the set of possible values that some random variables (random object) may take. Usually X will be a subset of , for example { 0 , 1 } , [0 , 1], [0 , ), , etc. I. Parametric Statistics. We will start by considering a family of distributions on X : • { θ , θ Θ } , indexed by parameter θ . Here, Θ is a set of possible parameters and probability θ describes chances of observing values from subset of X, i.e. for A X , θ ( A ) is a probability to observe a value from A . Typical ways to describe a distribution: probability density function (p.d.f.), probability function (p.f.), cumulative distribution function (c.d.f.). For example, if we denote by N ( α, σ 2 ) a normal distribution with mean α and variance σ 2 , then θ = ( α, σ 2 ) is a parameter for this family and Θ = × [0 , ). Next we will assume that we are given
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This note was uploaded on 10/11/2009 for the course STATISTICS 18.443 taught by Professor Dmitrypanchenko during the Spring '09 term at MIT.

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lec1 - Lecture 1 Estimation theory. 1.1 Introduction Let us...

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