AP Statistics Curriculum 2007 Bayesian Prelim

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What is commonly called '''Bayesian Statistics''' is a very special application of Bayes Theorem.
What is commonly called '''Bayesian Statistics''' is a very special application of Bayes Theorem.
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We will examine a number of examples in this Chapter, but to illustrate generally, imagine that '''x''' is a fixed collection of data that has been realized from under some known density, <math>f(\cdot)</math> that takes a parameter, <math>\mu</math> whose value is not certainly known.
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We will examine a number of examples in this Chapter, but to illustrate generally, imagine that '''x''' is a fixed collection of data that has been realized from under some known density, <math>f(\cdot)</math>, that takes a parameter, <math>\mu</math>, whose value is not certainly known.
Using Bayes Theorem we may write
Using Bayes Theorem we may write
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<math>f(\mu|\mathbf{x}) = \frac{f(\mathbf{x}|\mu) \cdot f(\mu)} { f(\mathbf{x}) }</math>
<math>f(\mu|\mathbf{x}) = \frac{f(\mathbf{x}|\mu) \cdot f(\mu)} { f(\mathbf{x}) }</math>
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In this formulation, we solve for <math>f(\mu|\mathbf{x})</math>, the "posterior" density of the population parameter <math>\mu</math>.
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In this formulation, we solve for <math>f(\mu|\mathbf{x})</math>, the "posterior" density of the population parameter, <math>\mu</math>.
For this we utilize the likelihood function of our data given our parameter, <math>f(\mathbf{x}|\mu) </math>, and, importantly, a density <math>f(\mu)</math>, that describes our "prior" belief in <math>\mu</math>.
For this we utilize the likelihood function of our data given our parameter, <math>f(\mathbf{x}|\mu) </math>, and, importantly, a density <math>f(\mu)</math>, that describes our "prior" belief in <math>\mu</math>.
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Since <math>\mathbf{x}</math> is fixed, <math>f(\mathbf{x})</math>, is a fixed number -- a "normalizing constant" so to assure that the posterior density integrates to one.
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Since <math>\mathbf{x}</math> is fixed, <math>f(\mathbf{x})</math> is a fixed number -- a "normalizing constant" so to ensure that the posterior density integrates to one.
<math>f(\mathbf{x}) = \int_{\mu} f(\mu \cap \mathbf{x}) d\mu =  \int_{\mu} f( \mathbf{x} | \mu ) f(\mu) d\mu </math>
<math>f(\mathbf{x}) = \int_{\mu} f(\mu \cap \mathbf{x}) d\mu =  \int_{\mu} f( \mathbf{x} | \mu ) f(\mu) d\mu </math>

Revision as of 20:11, 23 July 2009

Bayes Theorem

Bayes theorem, or "Bayes Rule" can be stated succinctly by the equality

P(A|B) = \frac{P(B|A) \cdot P(A)} {P(B)}

In words, "the probability of event A occurring given that event B occurred is equal to the probability of event B occurring given that event A occurred times the probability of event A occurring divided by the probability that event B occurs."

Bayes Theorem can also be written in terms of densities over continuous random variables. So, if f(\cdot) is some density, and X and Y are random variables, then we can say

f(Y|X) = \frac{f(X|Y) \cdot f(Y)} { f(X) }

What is commonly called Bayesian Statistics is a very special application of Bayes Theorem.

We will examine a number of examples in this Chapter, but to illustrate generally, imagine that x is a fixed collection of data that has been realized from under some known density, f(\cdot), that takes a parameter, μ, whose value is not certainly known.

Using Bayes Theorem we may write

f(\mu|\mathbf{x}) = \frac{f(\mathbf{x}|\mu) \cdot f(\mu)} { f(\mathbf{x}) }

In this formulation, we solve for f(\mu|\mathbf{x}), the "posterior" density of the population parameter, μ.

For this we utilize the likelihood function of our data given our parameter, f(\mathbf{x}|\mu) , and, importantly, a density f(μ), that describes our "prior" belief in μ.

Since \mathbf{x} is fixed, f(\mathbf{x}) is a fixed number -- a "normalizing constant" so to ensure that the posterior density integrates to one.

f(\mathbf{x}) = \int_{\mu} f(\mu \cap \mathbf{x}) d\mu =  \int_{\mu} f( \mathbf{x} | \mu ) f(\mu) d\mu

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